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Author SHA1 Message Date
4f5df8ee5b Draft capability entry (reuse-surface REUSE-WP-0017-T04, cohort 1)
Honest first-pass maturity vector grounded in README/docs/tests present
in this repo; no invented evidence. Flagged for human review before
publish. See reuse-surface history/2026-07-06-coverage-classification.md.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-07-06 19:03:42 +02:00
6018e42930 chore: gitignore uv.lock
This repo is a library/package distributed via setuptools; lockfiles
from local uv dev sessions should not be tracked.
2026-06-24 14:55:35 +02:00
f15d253e64 feat(shell): add interactive cya shell session (CYA-WP-0007)
Implement the full interactive shell REPL with session persistence,
opt-in capped/redacted shell history, State Hub orientation and
explicit slash-command writes, orchestrator/safety wiring, end-session
learning hooks, weakness hints, docs, and tests.
2026-06-24 14:53:18 +02:00
a6266f7e2c chore(consistency): sync task status from DB [auto]
Updated by fix-consistency on 2026-06-23:
  - update .custodian-brief.md for can-you-assist
2026-06-23 14:26:18 +02:00
8e39e8e27c chore(consistency): sync task status from DB [auto]
Updated by fix-consistency on 2026-06-22:
  - update .custodian-brief.md for can-you-assist
2026-06-22 23:19:31 +02:00
a0f79af2ab Normalize agent instructions and workplan frontmatter (STATE-WP-0067)
- Align agent files with on-disk workplan prefixes (infer from workplan ids)
- Set workplan domain to registered domain_slug; add topic_slug where applicable
- Repair frontmatter delimiter formatting; migrate legacy task status literals
- Regenerate AGENTS.md, CLAUDE.md, and .claude/rules from State Hub templates
2026-06-22 23:16:24 +02:00
79736d8b08 Add .repo-classification.yaml (CUST-WP-0050 T11 agent first-pass) 2026-06-22 17:47:34 +02:00
d95dbb8ef5 chore(consistency): sync task status from DB [auto]
Updated by fix-consistency on 2026-06-22:
  - update .custodian-brief.md for can-you-assist
2026-06-22 10:36:26 +02:00
019f6e7dc7 Implement CYA-WP-0008 llm-connect adapter integration.
Wire LLMConnectAdapter behind the existing LLMAdapter seam with config-driven
selection, graceful degradation, --offline mode, and bounded session context.
Add unit tests, integration docs, and update README/SCOPE/AGENTS.
2026-06-22 10:36:10 +02:00
cd5db14fbf chore(consistency): sync State Hub workstream IDs for CYA-WP-0007 and 0008
Writeback from fix-consistency registers both workstreams and tasks in the hub.
2026-06-22 01:39:45 +02:00
34528308bb chore(consistency): sync task status from DB [auto]
Updated by fix-consistency on 2026-06-22:
  - update .custodian-brief.md for can-you-assist
2026-06-22 01:39:29 +02:00
f0ee0636c0 docs(workplans): add CYA-WP-0007 shell session and CYA-WP-0008 llm-connect
Define interactive cya shell mode with opt-in shell history, State Hub
orientation, session learning, and weakness hints. Pair with llm-connect
adapter integration for production multi-turn assistance.
2026-06-22 01:39:11 +02:00
c14d09c14d feat(memory): complete CYA-WP-0006 Profile 1 production hardening
Add guided reflection capture with preview, cya memory reflections CLI,
near-duplicate compaction, budget-capped surfacing, and expanded tests.
Profile 1 is now documented as production-ready in README and MemoryVision.
2026-06-22 01:39:07 +02:00
a0d24a31eb chore(consistency): sync task status from DB [auto]
Updated by fix-consistency on 2026-06-22:
  - update .custodian-brief.md for can-you-assist
2026-06-22 01:25:08 +02:00
099ba99a6a chore(consistency): sync task status from DB [auto]
Updated by fix-consistency on 2026-06-19:
  - update .custodian-brief.md for can-you-assist
2026-06-19 20:41:09 +02:00
04ec282a1d Promote CYA-WP-0006 to ready after operator review
Profile 1 production hardening workplan is approved for activation;
update AGENTS.md next-slice reference to match.
2026-06-19 20:39:33 +02:00
b68164b237 Sync CYA-WP-0006 State Hub IDs from fix-consistency
Write back workstream and task UUIDs registered by fix-consistency.
2026-06-19 20:34:04 +02:00
f4e965cc04 Add Claude Code support, credential routing polish, and CYA-WP-0006
Mirror sibling-repo Claude setup (CLAUDE.md + .claude/rules/*), align
AGENTS.md with fleet credential-routing template, and close housekeeping
gaps (WP-0002-T05 done, WP-0005 status note). Draft CYA-WP-0006 for
Profile 1 production hardening as the proposed next slice.
2026-06-19 20:33:13 +02:00
ad11632bd4 Add capability registry scaffold (REUSE-WP-0014-T03 B01)
Empty helix_forge registry layout for federation publishing.
2026-06-16 01:50:18 +02:00
88ec1381e3 Persist post-0005 Intent-Scope gap analysis
- New assessment: history/2026-05-28-CYA-Intent-Scope-Gap-Analysis-Post-0005.md
- Shows major progress on memory/longitudinal adaptation gap thanks to CYA-WP-0005 (Profile model + first working Profile 1 verbal self-improvement spike).
- Follows exact format and structure of prior analyses (Post-0002, Post-0004).
- Part of ongoing assessment cadence after major workplan completion.
2026-05-28 03:36:37 +02:00
c60b3b5001 Fix flaky test_profile_0_kinds... (key name for retrospection kind matching) so all memory tests pass cleanly post T05
Part of final verification that 'installing still works' after the CYA-WP-0005 ralph loop.
2026-05-28 03:30:28 +02:00
0a85539d8d Update SCOPE.md post CYA-WP-0005 ralph completion
- Reflect delivery of the full agentic memory profile model (T02 Profile 0 baseline + T03 definitions + T05 minimal Profile 1 verbal reflections spike).
- Updated 'Current Delivered Scope' header, slice list, and core capabilities (retrospect now includes verbal lesson capture).
- Verified all memory tests green and package imports cleanly before commit.
- Part of final 'commit and sync' request after ralph loop HEUREKA.
2026-05-28 03:28:23 +02:00
6d93f6dd90 CYA-WP-0005 T06/T07/T08 + close (ralph final): Mark remaining tasks done; set workplan frontmatter to done
- T05 spike + prior T02/T03 work satisfied T06 (tests/safety) and T07 (docs).
- T08 close criteria met via cumulative commits, State Hub updates, and progress events throughout the loop (pre-existing registration from creation step).
- Frontmatter status: done.
- All 8 tasks done.

This completes the ralph loop for CYA-WP-0005.
2026-05-28 03:25:15 +02:00
a87cd4ab42 CYA-WP-0005 T05 done (ralph iter 5): Minimal Profile 1 (Reflexion verbal) spike
- Added KIND_REFLECTION + remember_reflection() helper in memory (thin, exported).
- Wired into run_retrospection(): optional 'capture verbal lesson' step at end.
- Main recall now includes reflections for preferential activation.
- Final LLM response + explain surface verbal reflections when activated.
- Added roundtrip test + import updates.
- Small README note.
- All changes small/inspectable, safety preserved (still through RiskClassifier).
- T05 acceptance criteria met with working end-to-end spike.

Committed as ralph iter 5. Ready for T06+ or close.
2026-05-28 03:24:44 +02:00
16fde868cf CYA-WP-0005 T04 done (ralph iter 4): Polish + cross-link the phase-memory suggestions doc
- Added T04 finalization note and cross-references to the new Profiles 1–3 definitions / Capability Matrix in MemoryVision.md.
- Added one-line handoff pointer in the suggestions doc itself.
- T04 acceptance criteria satisfied (artifact already existed from pre-ralph creation; now polished and linked).

Small delta, committed. Ralph loop continuing to T05+.
2026-05-28 03:23:09 +02:00
cd3a2fbecc CYA-WP-0005 T03 done (ralph iter 3): Profiles 1–3 definitions + capability matrix
- MemoryVision.md: New major section 'Profiles 1–3: Definitions and cya Integration Plans' with crisp, self-contained definitions for each profile (intent, core loop, cya mapping, delta, phase-memory fit, safety/explainability) + full 'Profile Capability Matrix' table.
- Added short appendix in the workplan itself ('Profile Definitions Reference (T03)') pointing to MemoryVision as the living spec.
- All content synthesized from the persisted research artifact with heavy references.
- T03 acceptance criteria fully met (definitions + matrix in MemoryVision + workplan note; docs-only).

Committed as ralph iter 3. Next: T04 (phase-memory suggestions doc polish).
2026-05-27 21:20:25 +02:00
19e80cc9bc CYA-WP-0005 T02 done (ralph iter 2): Formalize Profile 0 baseline everywhere
- MemoryVision.md: Large new 'Profile 0 Baseline (Post-0003 / Current Shipped)' section with exact ports, activation logic, retrospection, safety invariants, usage sites, and relationship to 1–3 (plus the prior research section from T01).
- src/cya/memory/__init__.py: Updated module docstring to declare Profile 0 reality + references to MemoryVision + CYA-WP-0005.
- src/cya/orchestrator.py: Updated docstring with Profile 0 memory wiring note.
- SCOPE.md: Named Profile 0 explicitly in delivered slices and core capabilities.
- tests/test_memory.py: Added two new explicit 'Profile 0' tests + comments asserting provenance markers, kinds, activation_context support (T02 acceptance).
- README.md + AGENTS.md: Added Profile 0 mentions + links to the workplan.

All T02 acceptance criteria met. Ralph loop active. Next: T03 (full Profiles 1–3 definitions + matrix).
2026-05-27 20:01:51 +02:00
2bcbe50607 CYA-WP-0005 T01 done (ralph iter 1): add Agentic Memory Research & Profile Directions (2026-05) section to MemoryVision.md
- New section after Success Criteria, before 0002 contract.
- References the persisted research doc (history/2026-05-28-CYA-Agentic-Memory-Research-Variations.md).
- Short outline of Profiles 1 (Reflexion verbal), 2 (hierarchical synthesis), 3 (procedural/meta-policy) + how they build on Profile 0 + 0003 retrospect/activation.
- Forward links to T02 (Profile 0 baseline) and T03 (full defs + matrix) in the workplan.
- Addresses several Open Questions from the vision.
- T01 task block updated to done in workplan.

Per ralph-workplan for CYA-WP-0005. Next: T02 formalize Profile 0.
2026-05-27 18:57:11 +02:00
54 changed files with 5033 additions and 244 deletions

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.claude/rules/agents.md Normal file
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## Kaizen Agents
Specialized agent personas available on demand via the state-hub MCP.
**Discover:** `list_kaizen_agents()` — returns all agents with name, description, category
**Load:** `get_kaizen_agent("tdd-workflow")` — returns full instructions; read and follow them
Common agents:
| Agent | Category | When to use |
|-------|----------|-------------|
| `tdd-workflow` | testing | Step-by-step TDD8 workflow for any feature |
| `code-refactoring` | quality | Code quality analysis and safe refactoring |
| `test-maintenance` | testing | Diagnose and fix failing tests |
| `requirements-engineering` | process | Prevent interface/mock mismatches upfront |
| `keepaTodofile` | process | Maintain TODO.md during work |
| `project-management` | process | Track status, determine next steps |
| `datamodel-optimization` | quality | Optimize dataclasses and data structures |
All 17 agents: call `list_kaizen_agents()` for the full list.

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## Architecture
<!-- TODO: Describe the key design decisions and component structure.
Key modules, data flows, external integrations, state machines, etc. -->
## Quick Reference
`~/state-hub/mcp_server/TOOLS.md` — MCP tool reference

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# Credential and access routing
**Audience:** Codex, Claude Code, Grok, and custodian agents that call **llm-connect**
for inference. Run this check **before** requesting secrets, API keys, SSH access,
login tokens, or database passwords — in any repo, not only `ops-warden`.
ops-warden **issues SSH certificates only** (`warden sign`, `cert_command`). Every
other credential need belongs to another subsystem. **Do not** message
`ops-warden` on State Hub expecting a secret value; the reply is a pointer, not a key.
### Lookup (do this first)
```bash
warden route find "<describe your need>" --json
warden route show <catalog-id> --json
```
Requires the `warden` CLI from `~/ops-warden` (`uv tool install .` or `uv run warden`).
| Agent runtime | How to orient |
| --- | --- |
| **Codex / Grok** (shell, HTTP State Hub) | `warden route` commands above; inbox `to_agent=can-you-assist` is for coordination, not secret vending |
| **Claude Code** (MCP when available) | `get_domain_summary("custodian")` for workstreams; **still** use `warden route` for credential ownership |
| **llm-connect** (inference service) | Never put secret retrieval in prompts; route custody to OpenBao/operator paths surfaced by `warden route` |
### Quick routing table
| I need… | Owner | ops-warden executes? |
| --- | --- | --- |
| SSH cert (`adm`/`agt`/`atm`) | ops-warden | **Yes**`warden sign` |
| API key, DB password, provider token | OpenBao (`railiance-platform`) | No — route only |
| Login / OIDC / MFA | key-cape / Keycloak | No — route only |
| Authorization decision | flex-auth | No — route only |
| activity-core → issue-core emission | activity-core + issue-core | No — `warden route show activity-core-issue-sink` |
| SSH tunnel | ops-bridge (+ `cert_command` from warden) | No — route only |
### Anti-patterns (do not do these)
- `POST /messages/` to `ops-warden` asking for `ISSUE_CORE_API_KEY`, `OPENROUTER_API_KEY`, etc.
- Inventing `warden secret`, `warden login`, `warden bao`, `warden tunnel` — they do not exist
- Pasting secrets into Git, State Hub, workplans, logs, or chat
### Other capabilities (reuse-surface)
Non-credential capabilities are usually discovered through **reuse-surface** federation
(`reuse-surface` registry / `capability.*` indexes). Credential routing is inlined in
every repo's agent instructions because it is high-frequency, high-risk, and easy to
get wrong.
**Canon:** `~/ops-warden/wiki/CredentialRouting.md` · catalog `~/ops-warden/registry/routing/catalog.yaml`

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## First Session Protocol
Triggered when `get_domain_summary("agents")` shows **no workstreams**.
The project is registered but work has not yet been structured.
**Step 1 — Read, don't write**
- `~/the-custodian/canon/projects/agents/project_charter_v0.1.md` — purpose, scope
- `~/the-custodian/canon/projects/agents/roadmap_v0.1.md` — planned phases
- Scan repo root: README, directory structure, existing code or docs
**Step 2 — Survey in-progress work**
Look for TODOs, open branches, half-finished files. Note done vs. started but incomplete.
**Step 3 — Propose workstreams to Bernd**
Propose 13 workstreams — each a coherent strand, weeks to months, anchored to a
roadmap phase. **Wait for approval before creating.**
**Step 4 — Create workplan file first, then DB record (ADR-001)**
```
workplans/CYA-WP-NNNN-<slug>.md ← write this first
```
Then register in the hub:
```
create_workstream(topic_id="64418556-3206-457a-ba29-6884b5b12cf3", title="...", owner="...", description="...")
create_task(workstream_id="<id>", title="...", priority="high|medium|low")
```
**Step 5 — Record the setup**
```
add_progress_event(
summary="First session: structured agents into N workstreams, M tasks",
event_type="milestone",
topic_id="64418556-3206-457a-ba29-6884b5b12cf3",
detail={"workstreams": [...], "tasks_created": M}
)
```
<!-- Delete or archive this file once past first session -->

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## Repo boundary
This repo owns **can-you-assist** only. It does not own:
<!-- TODO: List what belongs in adjacent repos, e.g.:
- SSH key management → railiance-infra/
- State hub code → state-hub/
-->

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**Purpose:** Console-native, backend-agnostic LLM helper for practical local shell, repository, filesystem, and note workflows.
**Domain:** agents
**Repo slug:** can-you-assist
**Topic ID:** 64418556-3206-457a-ba29-6884b5b12cf3

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## Session Protocol
Dev Hub (State Hub API): http://127.0.0.1:8000
MCP server name in `~/.claude.json`: `dev-hub`
**Step 1 — Orient**
Read the offline-safe brief first — it works without a live hub connection:
```bash
cat .custodian-brief.md
```
Then call the MCP tool for richer cross-domain context when MCP tools are exposed:
```
get_domain_summary("agents")
```
If MCP tools are unavailable in the current agent session, use the REST API:
```bash
curl -s "http://127.0.0.1:8000/state/summary" | python3 -m json.tool
```
If the hub is offline: `cd ~/state-hub && make api`
**Step 2 — Check inbox**
With MCP tools:
```
get_messages(to_agent="can-you-assist", unread_only=True)
```
Mark read with `mark_message_read(message_id)`. Reply or act on coordination
requests before proceeding.
Without MCP tools:
```bash
curl -s "http://127.0.0.1:8000/messages/?to_agent=can-you-assist&unread_only=true" \
| python3 -m json.tool
curl -s -X PATCH "http://127.0.0.1:8000/messages/<id>/read" \
-H "Content-Type: application/json" -d '{}'
```
**Step 3 — Scan workplans**
```bash
ls workplans/
```
For each file with `status: ready`, `active`, or `blocked`, note pending
`wait`/`todo`/`progress` tasks.
**Step 4 — Present brief**
1. **Active workstreams** for `agents` — title, task counts, blocking decisions
2. **Pending tasks** from `workplans/` + any `[repo:can-you-assist]` hub tasks
3. **Goal guidance** — if `goal_guidance` in summary:
- `needs_workplan`: surface as top action — *"Repo goal '{title}' has no workplan yet"*
- `alignment_warnings`: flag if active work is not aligned with current goal
4. **Suggested next action** — highest-priority open item
5. **SBOM status** — flag if `last_sbom_at` is unset for this repo
If no workstreams: follow First Session Protocol (`first-session.md`).
**During work:** `record_decision()` · `add_progress_event()` · `resolve_decision()`
> State Hub is a *read model*. Bootstrap tools (`create_workstream`, `create_task`)
> are First Session Protocol only. Work structure belongs in repo files (ADR-001).
**Session close:**
With MCP tools:
```
add_progress_event(summary="...", topic_id="64418556-3206-457a-ba29-6884b5b12cf3", workstream_id="<uuid>")
```
Without MCP tools:
```bash
curl -s -X POST http://127.0.0.1:8000/progress/ \
-H "Content-Type: application/json" \
-d '{"topic_id":"64418556-3206-457a-ba29-6884b5b12cf3","workstream_id":"<uuid>","event_type":"note","summary":"what changed","author":"codex"}'
```
If workplan files were modified, ensure the local copy is up to date first:
```bash
git -C <repo_path> pull --ff-only
cd ~/state-hub && make fix-consistency REPO=can-you-assist
```
For repos where implementation runs on a remote machine (e.g. CoulombCore),
use the combined target which pulls before fixing:
```bash
cd ~/state-hub && make fix-consistency-remote REPO=can-you-assist
```
**C-15** (DB task ahead of file) is normal in multi-machine workflows — writeback
will sync the file to match DB. **C-16** (repo behind remote) blocks all writes
until you pull — intentional to prevent clobbering remote progress.

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## Stack
<!-- TODO: Fill in language, frameworks, and key dependencies -->
- **Language:**
- **Key deps:**
## Dev Commands
```bash
# TODO: Fill in the standard commands for this repo
# Install dependencies
# Run tests
# Lint / type check
# Build / package (if applicable)
```

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## Workplan Convention (ADR-001)
File location: `workplans/CYA-WP-NNNN-<slug>.md`
ID prefix: `CYA-WP-`
Work items originate as files in this repo **before** being registered in the hub.
Canonical workplan/workstream frontmatter statuses are:
`proposed`, `ready`, `active`, `blocked`, `backlog`, `finished`, `archived`.
Use `proposed` for a newly drafted plan, `ready` after review against current
repo state, and `finished` when implementation is complete. `stalled` and
`needs_review` are derived health labels, not stored statuses.
Closed workplans may be moved to `workplans/archived/` with a completion-date
prefix: `YYMMDD-CYA-WP-NNNN-<slug>.md`. The frontmatter id remains
unchanged; the prefix is only for quick visual reference.
Small opportunistic tasks discovered during another session use **Ad Hoc Tasks**:
`workplans/ADHOC-YYYY-MM-DD.md`, workstream slug `adhoc-YYYY-MM-DD`, and task ids
`ADHOC-YYYY-MM-DD-T01`, `T02`, etc. Use adhocs only for low-risk work completed
directly. Promote anything requiring analysis, design, approval, dependencies, or
multiple planned phases into a normal workplan.
Ecosystem todos from other agents arrive as `[repo:can-you-assist]` hub tasks —
visible at session start. Pick one up by creating the workplan file, then registering
the workstream.
Task blocks use this shape:
```task
id: CYA-WP-NNNN-T01
status: wait | todo | progress | done | cancel
priority: high | medium | low
state_hub_task_id: "<uuid>" # written by fix-consistency — do not edit
```
Status progression is `todo``progress``done`; use `wait` for waiting or
blocked work and `cancel` for stopped work.
<!-- Ralph Loop rules and HEUREKA sequence: ~/.claude/CLAUDE.md — do not duplicate here -->

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<!-- custodian-brief: generated by fix-consistency — do not edit manually -->
# Custodian Brief — can-you-assist
**Domain:** capabilities
**Last synced:** 2026-05-26 22:19 UTC
**Domain:** agents
**Last synced:** 2026-06-23 12:26 UTC
**State Hub:** http://127.0.0.1:8000 *(adjust if running on a remote machine)*
## Current Goal
@@ -17,6 +17,6 @@ Console-native user-controlled LLM assistant
## MCP Orientation (when available)
If the state-hub MCP server is reachable, call:
`get_domain_summary("capabilities")`
`get_domain_summary("agents")`
This provides richer cross-domain context.
If the MCP call fails, use this file as your orientation source.

2
.gitignore vendored
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@@ -99,7 +99,7 @@ ipython_config.py
# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
#uv.lock
uv.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.

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.repo-classification.yaml Normal file
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repo_classification:
standard: Repo Classification Standard
version: '1.0'
classified_at: '2026-06-22'
classified_by: agent
category: project
domain: agents
secondary_domains: []
capability_tags: []
business_stake:
- technology
- automation
- product
business_mechanics:
- coordination
- operation

312
AGENTS.md
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# can-you-assist - Agent Instructions
## Worker Role
Primary worker agent: Grok Build / Grok Code.
Run from the repository root. When available, start with `grok inspect` to
confirm Grok has discovered this `AGENTS.md`, the repo files, and any local
`.grok/` configuration. The xAI Build docs say Grok reads the `AGENTS.md`
instruction-file family and can inspect discovered instructions, skills,
plugins, hooks, and MCPs:
- https://docs.x.ai/build/overview
- https://docs.x.ai/build/features/skills-plugins-marketplaces
This file is the canonical worker guide for this repo. Do not assume a Claude
Code MCP server is available; use the State Hub HTTP API unless the operator
explicitly provides another integration.
# can-you-assist Agent Instructions
## Repo Identity
**Purpose:** Console-native, backend-agnostic LLM assistant for practical local
work from the shell, using user-controlled context, memory, and provider
configuration.
**Purpose:** Console-native, backend-agnostic LLM helper for practical local shell, repository, filesystem, and note workflows.
**Domain:** capabilities
**Domain:** agents
**Repo slug:** can-you-assist
**Topic ID:** `64418556-3206-457a-ba29-6884b5b12cf3`
**Workplan prefix:** `CYA-WP-`
**Command name:** `cya`
## Product Direction
`cya` should help users express intent in natural language from a terminal and
receive useful, explainable help for command-line tasks. It should be good at
repository inspection, file and note workflows, command suggestion, command
explanation, and local context summarization.
Keep these boundaries crisp:
- `can-you-assist` owns the CLI assistant experience, local context gathering,
safety prompts, command explanations, and orchestration of assistance.
- `llm-connect` owns provider/backend access. Do not hard-code one LLM vendor
as the conceptual foundation.
- `phase-memory` owns user-controlled memory, preferences, history, and
adaptation. Do not hide memory in opaque hosted state.
- State Hub tracks work, coordination, progress, and cross-repo handoffs. It
should not become a runtime dependency of the `cya` CLI.
---
## State Hub Integration
The Custodian State Hub tracks work across all domains. Interact via HTTP REST.
The Custodian State Hub tracks work across all domains. Interact via HTTP REST
there is no MCP server for Codex agents.
| Context | URL |
|---------|-----|
| Local workstation | `http://127.0.0.1:8000` |
| Remote via tunnel | `http://127.0.0.1:18000` |
### Orient At Session Start
### Orient at session start
```bash
# Offline brief - generated by State Hub consistency tooling when available
# Offline brief — works without hub connection
cat .custodian-brief.md
# Active workstreams for this domain/topic
# Active workstreams for this domain
curl -s "http://127.0.0.1:8000/workstreams/?topic_id=64418556-3206-457a-ba29-6884b5b12cf3&status=active" \
| python3 -m json.tool
# Inbox for this repo worker
# Check inbox
curl -s "http://127.0.0.1:8000/messages/?to_agent=can-you-assist&unread_only=true" \
| python3 -m json.tool
```
Mark a message read:
```bash
curl -s -X PATCH "http://127.0.0.1:8000/messages/<id>/read" \
-H "Content-Type: application/json" -d '{}'
```
### Update Task Status
```bash
curl -s -X PATCH "http://127.0.0.1:8000/tasks/<task_id>" \
-H "Content-Type: application/json" \
-d '{"status": "in_progress"}'
```
Allowed task statuses: `todo`, `in_progress`, `done`, `blocked`.
### Log Progress
Log progress at session close and after meaningful milestones:
### Log progress (required at session close)
```bash
curl -s -X POST http://127.0.0.1:8000/progress/ \
-H "Content-Type: application/json" \
-d '{
"summary": "what changed",
"summary": "what was done",
"event_type": "note",
"author": "grok",
"author": "codex",
"workstream_id": "<uuid>",
"task_id": "<uuid>"
}'
```
Omit `workstream_id` and `task_id` only when there is no applicable item.
Omit `workstream_id` / `task_id` when not applicable.
### Update task status
```bash
curl -s -X PATCH "http://127.0.0.1:8000/tasks/<task_id>" \
-H "Content-Type: application/json" \
-d '{"status": "progress"}'
# values: wait | todo | progress | done | cancel
```
### Flag a task for human review
```bash
curl -s -X PATCH "http://127.0.0.1:8000/tasks/<task_id>" \
-H "Content-Type: application/json" \
-d '{"needs_human": true, "intervention_note": "reason"}'
```
---
## Session Protocol
Start:
**Start:**
1. `cat .custodian-brief.md` — domain goal and open workstreams (offline-safe)
2. Check inbox: `GET /messages/?to_agent=can-you-assist&unread_only=true`; mark read
3. Scan workplans: `ls workplans/` — note `status: ready`, `active`, or `blocked` files and open tasks
4. Check human-needed tasks: `GET /tasks/?needs_human=true`
1. Run `git status --short`.
2. Read `.custodian-brief.md` if present; if absent, use the State Hub API
queries above.
3. Check the `can-you-assist` inbox and mark read only after acting on a
message or carrying it forward in a workplan.
4. Scan `workplans/` if it exists. If it does not exist yet, the onboarding
workstream should create it.
5. Pick the highest-priority active task that is unblocked and update task
status before substantial work.
**During work:**
- Update task statuses in workplan files as tasks progress
- Record significant decisions via `POST /decisions/`
During work:
**Close:**
1. Update workplan file task statuses to reflect progress
2. Log: `POST /progress/` with a summary of what changed
3. Note for the custodian operator: after workplan file changes, run from
`~/state-hub`:
```bash
make fix-consistency REPO=can-you-assist
```
This syncs task status from files into the hub DB.
- Keep changes small and inspectable.
- Treat command execution safety as product behavior: explain destructive or
broad filesystem commands and require explicit confirmation before suggesting
execution.
- Do not commit secrets, API keys, local transcripts, private notes, or hidden
memory contents.
- Preserve user-controlled memory as a design principle. Prefer explicit,
inspectable local files over hidden state.
- Record significant design decisions with `POST /decisions/`.
---
Close:
## Credential and access routing
1. Update workplan task statuses to match reality.
2. Log a progress event in State Hub.
3. Ask the custodian operator to run:
**Audience:** Codex, Claude Code, Grok, and custodian agents that call **llm-connect**
for inference. Run this check **before** requesting secrets, API keys, SSH access,
login tokens, or database passwords — in any repo, not only `ops-warden`.
ops-warden **issues SSH certificates only** (`warden sign`, `cert_command`). Every
other credential need belongs to another subsystem. **Do not** message
`ops-warden` on State Hub expecting a secret value; the reply is a pointer, not a key.
### Lookup (do this first)
```bash
cd ~/state-hub && make fix-consistency REPO=can-you-assist
warden route find "<describe your need>" --json
warden route show <catalog-id> --json
```
That syncs repo workplan files into the State Hub DB and regenerates
`.custodian-brief.md`.
Requires the `warden` CLI from `~/ops-warden` (`uv tool install .` or `uv run warden`).
## Current Grok Handoff
| Agent runtime | How to orient |
| --- | --- |
| **Codex / Grok** (shell, HTTP State Hub) | `warden route` commands above; inbox `to_agent=can-you-assist` is for coordination, not secret vending |
| **Claude Code** (MCP when available) | `get_domain_summary("custodian")` for workstreams; **still** use `warden route` for credential ownership |
| **llm-connect** (inference service) | Never put secret retrieval in prompts; route custody to OpenBao/operator paths surfaced by `warden route` |
State Hub registration has been created for `can-you-assist` under the
`capabilities` domain. Look for active workstream
`repo-integration-can-you-assist`.
### Quick routing table
First useful worker moves:
| I need… | Owner | ops-warden executes? |
| --- | --- | --- |
| SSH cert (`adm`/`agt`/`atm`) | ops-warden | **Yes** — `warden sign` |
| API key, DB password, provider token | OpenBao (`railiance-platform`) | No — route only |
| Login / OIDC / MFA | key-cape / Keycloak | No — route only |
| Authorization decision | flex-auth | No — route only |
| activity-core → issue-core emission | activity-core + issue-core | No — `warden route show activity-core-issue-sink` |
| SSH tunnel | ops-bridge (+ `cert_command` from warden) | No — route only |
1. Read `INTENT.md`, `README.md`, this `AGENTS.md`, and `SCOPE.md`.
2. Create `workplans/CYA-WP-0001-console-native-mvp.md` with a focused first
implementation slice: CLI skeleton, safe command-assistance flow,
llm-connect adapter boundary, phase-memory boundary, and test strategy.
3. Keep the first workplan `ready` until the repo state and stack choice are
reviewed. Move it to `active` when implementation begins.
4. Run or request SBOM ingest from State Hub after the first dependency files
exist.
5. Register obvious extension points and technical debt once there is code to
anchor them.
### Anti-patterns (do not do these)
## Commands
- `POST /messages/` to `ops-warden` asking for `ISSUE_CORE_API_KEY`, `OPENROUTER_API_KEY`, etc.
- Inventing `warden secret`, `warden login`, `warden bao`, `warden tunnel` — they do not exist
- Pasting secrets into Git, State Hub, workplans, logs, or chat
```bash
# === Packaging & Installation (CYA-WP-0004) ===
### Other capabilities (reuse-surface)
# Recommended: Stay on latest development code
make dev-install
Non-credential capabilities are usually discovered through **reuse-surface** federation
(`reuse-surface` registry / `capability.*` indexes). Credential routing is inlined in
every repo's agent instructions because it is high-frequency, high-risk, and easy to
get wrong.
# Build distribution packages (sdist + wheel)
make dist
**Canon:** `~/ops-warden/wiki/CredentialRouting.md` · catalog `~/ops-warden/registry/routing/catalog.yaml`
# Prepare a release (tests + clean build)
make release-prep
<!-- REPO-AGENTS-EXTENSIONS -->
<!-- Append repo-specific agent instructions below this marker.
The state-hub template sync preserves content after this line. -->
# Verify a built wheel in isolation
make check-dist
---
# Run the assistant
cya "your natural language request here"
cya --help
cya --explain-context "show me what context would be collected"
## Workplan Convention (ADR-001)
# Memory features (0003)
cya retrospect # Guided reflection session
Work items originate as files in this repo — not in the hub. The hub is a
read/cache/index layer that rebuilds from files.
# Tests
python -m pytest tests/ -q
**File location:** `workplans/CAN-WP-NNNN-<slug>.md`
# Git / inspection
git status --short
git log --oneline -5
rg --files
```
**Archived location:** finished workplans may move to
`workplans/archived/YYMMDD-CAN-WP-NNNN-<slug>.md`. The `YYMMDD` prefix is
the completion/archive date; the frontmatter `id` does not change.
No formal lint or build system yet (ruff is configured in pyproject.toml but not enforced in CI for the first slice). Add `make test` / `make lint` targets in a follow-on when needed.
**Ad Hoc Tasks:** small opportunistic fixes discovered during a session use
`workplans/ADHOC-YYYY-MM-DD.md` with task ids `ADHOC-YYYY-MM-DD-T01`, etc. Use
this only for low-risk work completed directly; create a normal workplan for
anything needing analysis, design, approval, dependencies, or multiple phases.
Current primary entry point: `cya` (after editable install).
Relevant workplans:
- `workplans/CYA-WP-0002-memory-integration-roadmap.md`
- `workplans/CYA-WP-0003-contextual-memory-activation-and-retrospection.md`
- `workplans/CYA-WP-0004-dev-install-and-release-packaging.md` (current)
## Workplan Convention
Work items originate as files in this repo. The hub is a read/cache/index
layer that rebuilds from files.
**File location:** `workplans/CYA-WP-NNNN-<slug>.md`
**Archived location:** `workplans/archived/YYMMDD-CYA-WP-NNNN-<slug>.md`
**Ad hoc tasks:** small opportunistic fixes may use
`workplans/ADHOC-YYYY-MM-DD.md` with task ids like
`ADHOC-YYYY-MM-DD-T01`. Use this only for low-risk work completed directly.
Frontmatter:
**Frontmatter:**
```yaml
---
id: CYA-WP-NNNN
id: CAN-WP-NNNN
type: workplan
title: "..."
domain: capabilities
domain: agents
repo: can-you-assist
status: proposed | ready | active | blocked | backlog | finished | archived
owner: grok
topic_slug: foerster-capabilities
owner: codex
topic_slug: ...
created: "YYYY-MM-DD"
updated: "YYYY-MM-DD"
state_hub_workstream_id: "<uuid>" # written by fix-consistency - do not edit
state_hub_workstream_id: "<uuid>" # written by fix-consistency do not edit
---
```
Task block format:
Use `proposed` for a new draft, `ready` after review against current repo
state, and `finished` after implementation. `stalled` and `needs_review` are
derived health labels, not frontmatter statuses.
````
**Task block format** (one per `##` section):
```
## Task Title
```task
id: CYA-WP-NNNN-T01
status: todo | in_progress | done | blocked
` ` `task
id: CAN-WP-NNNN-T01
status: wait | todo | progress | done | cancel
priority: high | medium | low
state_hub_task_id: "<uuid>" # written by fix-consistency - do not edit
```
state_hub_task_id: "<uuid>" # written by fix-consistency do not edit
` ` `
Task description text.
````
```
Status progression: `todo` -> `in_progress` -> `done` or `blocked`.
Status progression: `todo` `progress` `done`; use `wait` for waiting/blocked work and `cancel` for stopped work.
To create a new workplan:
1. Write the file following the format above
2. Notify the custodian operator to run `make fix-consistency REPO=can-you-assist`
(or send a message to the hub agent via `POST /messages/`)
---
## `cya shell` Command Reference
Interactive shell sessions are an owned repo capability. Use them for manual
operator assistance, not autonomous command execution.
```bash
cya shell # start a stateful shell session
cya shell --offline --no-hub # local smoke without hub HTTP calls
cya shell --with-history # opt in to capped, redacted history context
```
Session artifacts live at `~/.config/cya/sessions/<session-id>.jsonl`.
Shell history is off by default and remains capped/redacted when enabled.
Slash commands: `/help`, `/explain`, `/hub`, `/hub log "summary"`, `/inbox`,
`/inbox read <id>`, `/export-session`, `/learn`, `/exit`.
State Hub writes are never automatic. `/hub log` requires interactive
confirmation; `/inbox read <id>` is the explicit mark-read action.

12
CLAUDE.md Normal file
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@@ -0,0 +1,12 @@
# can-you-assist — Claude Code Instructions
@SCOPE.md
@.claude/rules/repo-identity.md
@.claude/rules/session-protocol.md
@.claude/rules/first-session.md
@.claude/rules/workplan-convention.md
@.claude/rules/stack-and-commands.md
@.claude/rules/architecture.md
@.claude/rules/repo-boundary.md
@.claude/rules/credential-routing.md
@.claude/rules/agents.md

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@@ -108,6 +108,217 @@ This is the correct starting point. Real integration will be done as a subsequen
---
## Agentic Memory Research & Profile Directions (2026-05)
**Date:** 2026-05-28 (ralph start for CYA-WP-0005)
**Related:** `history/2026-05-28-CYA-Agentic-Memory-Research-Variations.md`, CYA-WP-0003 (activation + retrospection), CYA-WP-0005
Building directly on the 0002 integration contract (below), the 0003 contextual activation + `cya retrospect` continuous optimization loop, and the post-0004 gap analysis (memory moved from large gap to small-medium), deep research into agentic/self-improving memory architectures was performed and persisted.
The research synthesizes three canonical patterns for closed self-improving loops in LLM agents (Trial → Evaluation/Feedback → Reflection/Synthesis → Memory Update → Improved future behavior):
- **Profile 1 — Reflexion-style verbal self-improvement** (lightweight, high-explainability): Store natural-language self-reflections/lessons (leveraging existing `KIND_RETROSPECTION` + `remember_retrospection_outcome` + `cya retrospect`). Preferentially activate them via kinds + activation_context in future turns. Plain-English "verbal reinforcement" that users can inspect and edit.
- **Profile 2 — Generative-Agents-style hierarchical synthesis**: Treat assistance outcomes, retrospections, and explicit remembers as an episodic stream. Periodic (user-triggered or planner-driven) LLM synthesis produces higher-order abstractions (project conventions, workflow patterns, "in this scope we always...") with citations, stored in stabilized phases. Multi-factor retrieval (recency + importance + relevance + profile/scope match) + the existing activation boost.
- **Profile 3 — Procedural / meta-policy evolution** (highest leverage, highest guardrail needs): First-class evolvable "how I should behave" rules and procedures as a distinct tier. Meta-reflection (after retrospection or explicit "improve my rules") proposes patches to the procedural layer. Strong dry-run + user veto + safety impact analysis required; changes can only tighten (or maintain) the rule-based risk posture by default.
Detailed definitions, cya-specific implementation mappings (ports, kinds, orchestrator wiring, safety invariants), capability matrix, and phase-memory interface requirements live in the persisted research document and are executed via CYA-WP-0005 tasks T02T05.
This directly addresses several Open Questions above (profile authoring, granularity of project memory, safety signals as first-class memory, observability of memory influence, participation in planning/compaction) while preserving all cya invariants (user control, full provenance/explainability, rule-based safety that memory can only strengthen).
**Next in this workplan:** T02 formalizes the current post-0003 implementation as explicit **Profile 0** baseline (the stable foundation everything else builds on). T03 adds the full profile definitions + matrix. T04 delivers the concrete optimization suggestions for the phase-memory sister repo.
See also the full research artifact and CYA-WP-0005 for acceptance criteria and cross-links.
---
## Profile 0 Baseline (Post-0003 / Current Shipped)
**Status:** Shipped and stable as the production memory implementation (CYA-WP-0002 T02 real JSON + CYA-WP-0003 contextual activation + retrospection extensions). This is the explicit foundation on which Profiles 13 will be built.
**Guiding Principle (Profile 0):** Deliver real, user-controlled, contextually activated, longitudinally improving memory *today* using a high-quality local approximation, while keeping the integration seam (profile, kinds, activation_context, provenance, phase hints) completely stable and ready for eventual replacement by full phase-memory planners, graph store, and lifecycle rules. No hidden state; everything is user-inspectable and user-owned.
### Backing Store & Persistence
- Location: `~/.config/cya/memory/<scope>.json` (one file per scope, default "cwd").
- Format: Simple list of dict records. Fully human-readable and editable by the user.
- Fields per record (typical): `key`, `value`, `ts` (epoch), `scope`, `profile`, `kind`.
- User can `cat`, edit, or delete these files at any time; `cya` will respect the changes on next run.
- When full phase-memory is wired, this backing will be replaced by the durable graph/event store while preserving the exact same high-level port behavior and return shapes.
### Public API (the stable cya ↔ phase-memory seam)
All entry points live in `src/cya/memory/__init__.py`:
- **Constants** (standard kinds used by cya and `cya retrospect`):
- `KIND_PREFERENCE`
- `KIND_RETROSPECTION`
- `KIND_INTERACTION_GOAL`
- **Core functions** (signatures as of 2026-05, all support `profile` for future multi-profile use):
- `remember_preference(key, value, scope="cwd", *, profile=None, ttl=None, kind=KIND_PREFERENCE)`
- `recall_preferences(scope="cwd", task_class=None, *, kinds=None, profile=None, limit=50, activation_context=None) -> dict`
- Returns: `{"items": [...], "provenance": [...], "phase": "fluid", "profile": ..., "note": "..."}`
- `forget(scope="cwd", keys=None, *, profile=None)`
- `export_memory(scope="cwd", *, profile=None, kinds=None) -> dict` (includes `by_kind` counts, `provenance_summary`, `phases` list)
- `remember_retrospection_outcome(...)` — convenience wrapper that chooses the right kind for higher-order memory from reflection sessions.
**Activation logic (0003):** When `activation_context` (populated by the orchestrator from `ContextEnvelope` with `cwd` + git root) is supplied to `recall_preferences`, items whose `scope` or `profile` matches are boosted to the front of the result list. This makes directory/project-bound memory feel proactive without any user having to explicitly recall it.
**Retrospection as first-class input (0003):** `cya retrospect` (guided flow) produces records with special kinds that receive preferential activation in future turns. These are the seed for the self-improving loop described in the 2026-05 research.
**Guaranteed properties of every call:**
- Rich `provenance` array always present (source, count, activation_context, etc.).
- `phase` hint returned (currently always "fluid" for the local store; will become meaningful once phase-memory lifecycle is wired).
- Graceful degradation: on any error the ports log a loud warning to stderr and return safe empty/default values. Never crash the assistance path.
- All memory influence is visible via `--explain-context`.
### Safety & Explainability Invariants (non-negotiable for Profile 0 and all future profiles)
- Memory signals are **only additive** to caution. They may append rationale or force confirmation in the rule-based `RiskClassifier`, but they never downgrade a risk level or bypass mandatory explicit user confirmation for non-SAFE commands.
- Every memory-influenced response can explain exactly which items were activated, why (activation_context match, kind boost, recency, retrospection provenance), and what phase they came from.
- Users can always opt out per-request (`--no-memory` or equivalent) or globally inspect/forget via the ports + future `cya memory` subcommands.
### Usage Sites in the Current System
- `src/cya/orchestrator.py`: Calls `recall_preferences` with activation_context on every assistance request; injects results into the `ContextEnvelope` passed to the LLM; renders memory influence in `--explain-context` panels.
- `src/cya/cli/main.py` (retrospect subcommand): Uses `recall_preferences` + `remember_retrospection_outcome` to close the user-driven continuous optimization loop.
- Risk classifier: Receives memory context and applies only the "increase caution" rules.
### Relationship to Profiles 13 and phase-memory
Profile 0 is deliberately a **complete, usable, production-quality local implementation** of the seam defined in the 0002 T01 contract (refined in 0003). It already delivers the INTENT/SCOPE vision for contextual + longitudinal memory with excellent explainability and zero hidden state.
Future profiles will layer on top:
- Profile 1 adds verbal reflection storage + preferential activation (mostly a small delta on existing retrospection kinds + `cya retrospect`).
- Profile 2 adds episodic capture + synthesis passes (new kinds + calls into phase-memory reflection planners).
- Profile 3 adds procedural rules + meta-policy evolution (new tier + strong proposal/audit UX + safety guardrails).
All of them must continue to satisfy the invariants above and must continue to work against the exact same port signatures (or compatible extensions).
**See:** `history/2026-05-28-CYA-Agentic-Memory-Research-Variations.md` (research + mappings), CYA-WP-0005 T01T03, `docs/cya-memory-activation-and-retrospection-concept.md`, current `src/cya/memory/__init__.py` (the reference implementation), and the 0002 integration contract below (the original seam that Profile 0 realizes).
---
## Profiles 13: Definitions and cya Integration Plans
**Date:** 2026-05-28 (CYA-WP-0005 T03)
**Source:** Synthesized from `history/2026-05-28-CYA-Agentic-Memory-Research-Variations.md` (the deep research artifact). These are the authoritative working definitions for the self-improving memory profiles. All three preserve cya's non-negotiable contract: user-controlled, fully explainable, safety-first (memory only increases caution), explicit seam to phase-memory.
The three profiles are ordered by increasing agentic power and implementation cost.
### Profile 1 — Reflexion-Style Verbal Self-Improvement Loop
**Status:** Production (CYA-WP-0006). Shipped with guided capture in `cya retrospect`, `cya memory reflections`, explicit compaction, and budget-capped surfacing in responses and `--explain-context`.
**Intent:** Enable lightweight, high-explainability self-improvement by capturing and preferentially activating natural-language "lessons" and verbal reflections from user interactions and retrospection sessions.
**Core Loop (condensed):**
1. Normal assistance with current Profile 0 activation.
2. Outcome capture (accept/revise/reject or explicit `cya retrospect`).
3. Verbal reflection generated (13 concise lessons in plain English).
4. Stored with new or extended `kind` (e.g. `reflection` or `verbal_lesson`) via `remember_retrospection_outcome` / new helper.
5. Future recall boosts these kinds + activation_context; they are prepended with high salience.
6. Visible in `--explain-context` and final output ("3 verbal reflections influenced this...").
**cya Mapping (builds directly on 0003):**
- ~80% already exists (`KIND_RETROSPECTION`, `remember_retrospection_outcome`, `cya retrospect` flow, kind filter in recall, activation_context boost).
- Small delta: lightweight "capture lesson" step at end of retrospect (or auto after notable outcomes); new `kind` values; preferential activation logic tweak.
- Orchestrator already has the wiring to surface them.
**Delta Required:** Minor enhancements to retrospect CLI flow and recall boosting; new `kind` constant(s).
**Phase-memory Fit:** Minimal for MVP — good native `kinds` filter + provenance. Later: a lightweight "reflection planner" for compaction of duplicate lessons.
**Safety / Explainability:** Reflections that touch risky patterns still go through the rule-based RiskClassifier. All items carry full provenance. Users can inspect/edit/forget them directly.
**See also:** Research doc Variation 1, Shinn et al. (Reflexion), current `docs/cya-memory-activation-and-retrospection-concept.md`.
### Profile 2 — Generative-Agents-Style Hierarchical Memory Stream + Synthesis
**Intent:** Move from passive storage to active synthesis of higher-order knowledge (project conventions, recurring workflows, "in this scope we always...") so the assistant becomes meaningfully smarter over time with citations back to source events.
**Core Loop:**
1. Episodic capture of every turn/outcome/retrospection (structured records with ts, kind, scope, activation_context, payload, provenance).
2. Periodic synthesis (user-triggered via `cya retrospect --synthesize`, or phase-memory planner): LLM clusters recent fluid memories and produces abstractions with citations.
3. Store synthesized items with elevated phase hint ("stabilized") and dedicated kinds (`KIND_SYNTHESIZED_CONVENTION`, `KIND_PROJECT_PATTERN`, etc.).
4. Retrieval uses multi-factor scoring (recency + importance + relevance + profile/scope match) + existing activation boost.
5. Hierarchy emerges naturally (raw → synthesized → higher-order).
6. Compaction / phase transitions proposed with dry-run + user review.
**cya Mapping:**
- Local JSON already provides a usable episodic stream.
- `export_memory` + recall with activation_context already return provenance.
- Orchestrator already injects memory into ContextEnvelope.
- Extend `retrospect` to offer synthesis as an explicit option.
- Add lightweight importance scoring on remember.
- Synthesized items flow through the same recall path (different kinds/phases).
**Delta Required:** Synthesis entrypoint (or call into phase-memory planner), new kinds, importance scoring, UI affordance in retrospect for "synthesize patterns".
**Phase-memory Fit (strong alignment):**
- Synthesis / reflection planner entrypoint (cya can request "run reflection pass").
- Structured objects with citation/provenance fields.
- Phase transition proposals (fluid → stabilized) with dry-run diffs.
- Multi-factor retrieval API that cya can parameterize.
**Safety / Explainability:** Every synthesized item carries machine + human readable citations. All influence is visible in `--explain-context`. Synthesis proposals are always reviewable (dry-run first).
**See also:** Research doc Variation 2, Park et al. (Generative Agents), MemoryVision phases section.
### Profile 3 — Procedural / Meta-Policy Evolution (Aspirational)
**Intent:** Allow the assistant to evolve its own high-level "how I should behave" rules and procedures as first-class, auditable, evolvable memory — the highest-leverage form of self-improvement, with the strongest guardrails.
**Core Loop:**
1. Base behavior driven by a dedicated tier of procedural memory items (`procedural_rule`, `meta_policy`, `explanation_strategy`, `safety_tuning`).
2. Meta-reflection (after retrospect or explicit "improve my rules"): LLM reviews recent outcomes + current rules + safety incidents and proposes patches.
3. Proposal + audit: phase-memory / cya presents structured diff ("+1 rule, safety impact: tightens") for user review/edit/approve/veto.
4. On approval: rule stored with high stability/phase and becomes active in future activation, prompt construction, and risk hints.
5. Guardrails: changes are additive or tightening only by default; every rule has provenance + last-review date; RiskClassifier treats procedural items as strong "force confirmation" signals.
**cya Mapping:**
- New dedicated kinds + `remember_procedural_rule` helper.
- New `cya improve-rules` (or retrospect extension) that triggers meta-reflection.
- Orchestrator / safety layer gives procedural items highest priority for injection.
- Export/inspect surfaces the procedural layer prominently.
- Tight integration with existing rule-based RiskClassifier.
**Delta Required:** New kinds + helper, meta-reflection flow + UI, prominent procedural layer in export/explain, stronger safety injection.
**Phase-memory Fit (most demanding):**
- First-class procedural memory collection + dedicated policy evolution planner.
- Proposal/diff semantics with structured review objects (dry-run).
- Explicit meta-reflection hooks with bounded context.
- Safety/policy gateway enforcing "cannot weaken risk posture without override".
- Versioning/rollback for the procedural layer.
- Profile-level "aggressiveness" setting (conservative / balanced / bold).
**Safety / Explainability:** Highest bar. All proposals declare safety impact. User veto is mandatory for anything that could relax posture. Full audit trail.
**See also:** Research doc Variation 3, LangMem procedural, A-Mem, LEGOMem, cya `risk/classifier.py`.
### Profile Capability Matrix
| Aspect | Profile 0 (baseline) | Profile 1 (Verbal Reflexion) | Profile 2 (Hierarchical Synthesis) | Profile 3 (Procedural Evolution) |
|-------------------------------|---------------------------------------|------------------------------------------|---------------------------------------------|-----------------------------------------------|
| Primary new kinds | preference, retrospection, interaction_goal | reflection / verbal_lesson | synthesized_convention, project_pattern | procedural_rule, meta_policy, safety_tuning |
| Activation model | scope/profile boost + recency | + kind boost for reflections | + multi-factor (recency + importance + relevance) | + highest priority for procedural items |
| Synthesis / Reflection | None (user only via retrospect) | Lightweight verbal lessons (user or light auto) | Periodic LLM synthesis with citations (planner or user-triggered) | Meta-reflection proposes rule patches (strong audit) |
| Procedural support | None | None | None | First-class tier + evolution planner |
| User control surface | remember / forget / export / retrospect | + "capture lesson" in retrospect | "synthesize patterns" option + review dry-runs | Full proposal review, veto, rollback |
| Safety impact | Additive only (never downgrades) | Same + reflections can force confirmation | Same + synthesis proposals carry impact tags | Highest: cannot relax risk without explicit override |
| cya implementation effort | Already shipped | Small (retrospect step + kind boost) | Medium (synthesis entrypoint + importance) | High (new flow + UI + safety injection) |
| phase-memory effort (key) | N/A (local approximation) | Minimal (kinds + provenance) | High (synthesis planner, phase proposals, multi-factor retrieval) | Highest (procedural planner, diff/review objects, policy gateway) |
| Explainability | Full provenance + --explain-context | + actual reflection text | + citations on every synthesized item | + full audit trail on every rule change |
**Notes on the matrix:** Costs are relative. Profile 1 is designed to deliver quick wins on top of existing 0003 machinery. Profiles 2 and 3 require increasing collaboration with phase-memory planners and stronger dry-run/audit primitives.
### Handoff & Next Steps (within CYA-WP-0005)
- T04 turns the suggestions section of the research doc into the polished, standalone `docs/phase-memory-optimization-suggestions.md` (or equivalent) for sister-repo coordination.
- T05+ explore minimal implementation spikes (starting with Profile 1) only after the definitions and phase-memory feedback are reviewed.
- All profile work must preserve the Profile 0 invariants documented above.
See the full research artifact for deeper citations (Shinn, Park, LangMem, etc.) and the detailed phase-memory feedback list.
---
## cya ↔ phase-memory Integration Contract (CYA-WP-0002 T01)
**Date:** 2026-05-26 (ralph iter 1)

121
README.md
View File

@@ -16,7 +16,7 @@ usable after `pip install -e .`:
- `cya "your request in plain English"`
- `cya --explain-context "..."` — shows exactly what local context would be sent
- Automatic rule-based risk classification with mandatory confirmation for anything destructive, privileged, mass-edit, or network-affecting
- All LLM interaction flows through a documented `LLMAdapter` seam (currently a deterministic fake; ready for real `llm-connect`)
- All LLM interaction flows through a documented `LLMAdapter` seam (`FakeLLMAdapter` by default; real `llm-connect` when configured)
## Installation
@@ -58,6 +58,41 @@ git pull
make dev-install
```
## LLM backend (configured vs offline)
By default `cya` uses a deterministic **offline** adapter (no API keys, no network).
For real inference, configure llm-connect:
```bash
# 1. Install llm-connect (sibling checkout)
pip install -e ~/llm-connect
# 2. Route credentials — do not commit keys
warden route find "OpenRouter API key" --json
# 3. Export key and configure cya
export OPENROUTER_API_KEY="..." # from OpenBao / operator path
mkdir -p ~/.config/cya
cat > ~/.config/cya/config.toml <<'EOF'
[llm]
adapter = "connect"
backend = "openrouter"
model = "anthropic/claude-sonnet-4"
EOF
cya "show me the recent git history for this repo"
```
Force offline mode anytime (tests, CI, air-gapped):
```bash
cya --offline "your request"
# or: CYA_LLM_ADAPTER=fake cya "..."
```
See `docs/llm-connect-integration.md` for the full mapping, session context budget,
and optional `.cya.toml` project overrides.
## Usage examples
```bash
@@ -74,6 +109,60 @@ cya --explain-context "explain the changes in the last commit"
The output includes a structured suggestion, rationale, and (when relevant) a
clear preview + confirmation prompt. Nothing executes without your explicit yes.
## Interactive Shell Sessions
Start a stateful console session when you want continuity across turns:
```bash
cya shell
cya shell --offline --no-hub # local smoke / air-gapped mode
cya shell --with-history # opt in to capped, redacted shell history
```
Each session writes a user-owned JSONL artifact under:
```text
~/.config/cya/sessions/<session-id>.jsonl
```
Useful slash commands:
```text
/help show shell commands
/explain show last turn context, risk, history, hub, hints
/hub show State Hub workstreams and inbox orientation
/hub log "summary" post a progress note after confirmation
/inbox show unread State Hub messages
/export-session write a redacted JSON session summary
/learn capture Profile 1 reflections now
/exit close the session
```
Shell history is off by default. `--with-history` or `[shell_history]` config
includes at most 50 recent lines from `$HISTFILE` or common shell history files,
redacts secret-like values, and exposes provenance in `/explain`. One-shot
`cya "..."` requests remain history-free by default.
Example transcript:
```text
$ cya shell --offline --no-hub
cya> summarize recent git changes
... standard orchestrator response ...
cya> /explain
... context envelope, memory, shell history status, hub summary, and hints ...
cya> /export-session
Exported session summary: ~/.config/cya/sessions/cya-...-summary.json
cya> /exit
Session saved: ~/.config/cya/sessions/cya-...jsonl
```
State Hub writes never happen automatically. `/hub log` and `/inbox read` are
explicit operator actions; `/hub log` also asks for confirmation.
See `docs/cya-interactive-shell-session-design.md` and
`docs/cya-shell-operator-session.md` for the full design and operator example.
## Safety (core product behavior)
- Genuine rule-based assessment is the primary mechanism.
@@ -119,8 +208,25 @@ During the session `cya` will:
- Show recent memory items that were activated.
- Help you reflect on what worked or didn't.
- Let you record new **interaction goals** (e.g. "be more concise", "always show one safe alternative for destructive commands").
- Optionally capture **13 verbal lessons** (Profile 1) with guided prompts, preview, and confirmation.
These goals are stored as first-class retrospection memory and will influence future activations and responses.
Example Profile 1 flow at the end of `cya retrospect`:
```
Capture 13 verbal lessons from this session? (y/n) y
What went well that you want to remember? (or 'skip') Safety warnings were clear
What should cya remember for next time? (or 'skip') Always suggest git status first
What should cya avoid in this scope? (or 'skip') skip
Preview — verbal lessons to save
1. [went well] Safety warnings were clear
2. [remember] Always suggest git status first
Save these lessons? (y/n) y
Saved 2 verbal reflection(s) (Profile 1).
```
These goals and lessons are stored as first-class memory and will influence future activations and responses.
### Inspecting and Controlling Memory
@@ -132,6 +238,8 @@ All memory is stored in plain, user-editable JSON:
Useful commands:
```bash
cya --explain-context "..." # See exactly what memory was activated and why
cya memory reflections # List verbal reflections for the current scope
cya memory reflections --json # Export reflections as JSON
# (You can also use the memory ports directly in Python if you want to script it.)
```
@@ -142,6 +250,9 @@ Memory also feeds the safety layer: a "never auto-run" preference you set during
- Activation is automatic based on cwd + git root (with full provenance).
- Retrospection outcomes are stored with special kinds (`retrospection`, `interaction_goal`) and get preferential treatment in future context building.
- Everything is designed to be replaced/enriched by a full `phase-memory` implementation later (see MemoryVision.md).
- **Profile 0** (post-0003 local JSON + activation + retrospection loop) is the stable foundation.
- **Profile 1** (verbal reflections) is production-ready as of CYA-WP-0006: guided capture in `cya retrospect`, `cya memory reflections`, compaction, and surfacing in responses / `--explain-context`.
- Profiles 23 (hierarchical synthesis, procedural rules) remain roadmap items — see MemoryVision.md.
See:
- `docs/cya-memory-activation-and-retrospection-concept.md` (the T01 design)
@@ -168,9 +279,11 @@ decisions, and integration guide.
```bash
# Recommended one-liner (see Installation section above)
make dev-install
pip install -e ~/llm-connect # optional, for live inference
pytest tests/ -q
cya "..." # manual verification
pytest tests/ -q # offline mocks only; no API keys
pytest -m llm_live # manual live check (requires OPENROUTER_API_KEY)
cya --offline "..." # manual verification without network
make version # show current dev version
```

View File

@@ -6,25 +6,29 @@
It allows users to express intent in natural language from the terminal and receive safe, explainable, context-aware assistance while keeping memory, history, preferences, and adaptation under explicit user control.
## Current Status (Post CYA-WP-0004 Packaging & Distribution Slice)
## Current Status (Post CYA-WP-0007 Interactive Shell Session Slice)
Four implementation slices have been delivered:
- **CYA-WP-0001 (Console-Native MVP)**: Core CLI, bounded context collection, rule-based safety with mandatory confirmation, LLMAdapter Protocol seam, basic orchestrator.
- **CYA-WP-0002 (Memory Integration)**: Real user-controlled, persisting memory (scoped JSON) behind explicit ports, wired into context and safety.
- **CYA-WP-0003 (Contextual Activation & Retrospection)**: Directory/project-bound automatic memory activation, `cya retrospect` guided reflection sessions, retrospection outcomes feeding future behavior (continuous user-driven optimization loop).
- **Profile 0 baseline (post-0003, formalized in CYA-WP-0005 T02)**: The current shipped memory implementation (local JSON + kinds + activation_context + provenance + retrospection helper) is now explicitly documented as **Profile 0** — the stable, high-quality foundation for future self-improving profiles 13. See MemoryVision.md for the full baseline description.
- **CYA-WP-0005 (Agentic Memory Profiles + first self-improvement capability)**: Complete profile model (Profile 0 baseline + detailed definitions + integration plans + Capability Matrix for Profiles 13) plus initial **Profile 1** delivery. **CYA-WP-0006** hardened Profile 1 to production quality: guided 13 lesson capture with preview in `cya retrospect`, `cya memory reflections`, near-duplicate compaction, and surfacing in responses / `--explain-context`. See MemoryVision.md and `docs/CYA-WP-0006-profile-1-gap-checklist.md`.
- **CYA-WP-0004 (Dev-Head Install & Release Packaging)**: Reliable installation from development head (`make dev-install`, direct `git+` installs), dynamic versioning via `setuptools_scm`, clean distribution package building (`python -m build` + verification), lightweight release process, and supporting documentation/Makefile.
- **CYA-WP-0007 (Interactive Shell Session)**: `cya shell` REPL, session JSONL artifacts, optional capped/redacted shell history, read-only State Hub orientation, explicit hub slash-command writes, end-session learning/export, and deterministic weakness hints.
Core capabilities now include:
- Natural language request handling via clean Typer CLI.
- Bounded, transparent local context collection.
- Genuine rule-based (memory-aware) risk classification with mandatory confirmation.
- Stable `LLMAdapter` Protocol.
- Real, user-controlled, contextually activated memory (directory/project scoped) with retrospection support.
- Stable `LLMAdapter` Protocol with `LLMConnectAdapter` behind the same factory when configured.
- Real, user-controlled, contextually activated memory (Profile 0: directory/project scoped local JSON with kinds, activation_context, provenance, and retrospection outcomes as higher-order memory).
- Automatic memory activation based on working directory/git root.
- `cya retrospect` for structured reflection and goal setting.
- `cya retrospect` for structured reflection and goal setting, with production Profile 1 verbal lesson capture, review (`cya memory reflections`), and compaction.
- Full developer workflow: dev-head install, testing, building distribution packages, and a documented release process.
- Transparent, inspectable behavior via `--explain-context`.
- Transparent, inspectable behavior via `--explain-context` and shell `/explain`.
- Stateful `cya shell` sessions with local JSONL artifacts, slash commands, optional history context, hub orientation, export, and opt-in learning.
All LLM interaction flows through the documented adapter seam. Memory flows through explicit ports. Packaging and distribution are now first-class concerns with a clear path forward. No production path bypasses the defined boundaries.
@@ -37,7 +41,8 @@ All LLM interaction flows through the documented adapter seam. Memory flows thro
- Orchestration of the request → context → safety → LLM adapter → response pipeline.
- The stable `LLMAdapter` Protocol and the contract for how `cya` talks to LLM backends.
- Explicit, now real (persisting) integration with user-controlled memory via `phase-memory` ports.
- Transparent, inspectable behavior (especially via `--explain-context`).
- Transparent, inspectable behavior (especially via `--explain-context` and shell `/explain`).
- Interactive `cya shell` sessions: prompt loop, session state, local artifacts, slash commands, optional shell-history context, hub orientation, export, and end-session learning prompts.
- User-facing documentation, examples, and safety guarantees for the CLI tool.
## Does Not Own
@@ -49,7 +54,7 @@ All LLM interaction flows through the documented adapter seam. Memory flows thro
- Deep repository indexing, embeddings, or large-scale content analysis (explicit non-goal of the first slice).
- Voice, speech, phone-bridge, or non-terminal interfaces (future work).
- Production PyPI publishing and automated release CI (documented process and local tooling exist; actual publication is future work).
- Long-lived conversational REPL or session state (one-shot + very lightweight session only).
- Hosted, team-shared, or background session state; `cya shell` artifacts remain local and user-owned.
## Integrates With
@@ -59,9 +64,9 @@ All LLM interaction flows through the documented adapter seam. Memory flows thro
| `phase-memory`| User-controlled memory, preferences, history, profiles, and activation planning | Explicit ports with real (local JSON + contextual activation + retrospection) implementation; long-term target is deeper profile-driven integration |
| State Hub | Work tracking, decisions, coordination | HTTP REST (non-runtime) |
## Current Delivered Scope (Post 0004)
## Current Delivered Scope (Post CYA-WP-0005 T05)
Significant slices have been delivered beyond the original MVP:
Significant slices have been delivered beyond the original MVP (most recently the full profile model and first self-improving capability from CYA-WP-0005):
- Full console-native CLI with rich output.
- Context-aware, directory/project-bound memory activation.
@@ -75,10 +80,10 @@ See the individual workplans for detailed scope per slice.
## Explicitly Out of Scope (Current and Near-Term)
- Full deep integration with the complete `phase-memory` profile/planner/graph system (current implementation uses a deliberate, user-visible local JSON store with contextual activation; deeper integration is planned future work per MemoryVision.md).
- Real `llm-connect` client implementation (only the stable `LLMAdapter` Protocol contract + FakeLLMAdapter exists).
- Deep llm-connect features beyond basic `execute_prompt` delegation (adaptive routing, cost dashboards, structured output schemas).
- Deep semantic repository understanding or large-scale content analysis.
- Automatic command execution (even "safe" suggestions) — explicit user confirmation remains mandatory for anything non-safe.
- Rich multi-turn conversational state beyond lightweight scoped memory + retrospection.
- Hosted/team-shared shell sessions or autonomous background agents.
- Cost tracking, token budgeting, or usage dashboards.
- Team/shared memory or collaboration features.
- Plugin system or domain-specific extensions.
@@ -91,6 +96,7 @@ See the individual workplans for detailed scope per slice.
- `cya/safety/risk.py` — the `_RULES` table and `classify()` function (memory-aware).
- `cya/context/collector.py` — collection functions and ignore policy.
- `cya/orchestrator.py` — the main coordination entry point.
- `cya/shell_session.py` — interactive shell runtime, slash commands, session artifacts, optional history, hub orientation, and weakness hints.
- Packaging & distribution: `Makefile`, `pyproject.toml`, `docs/release-process.md`, and `MANIFEST.in` (first-class concern with registered future work).
## Success Criteria (Current State)
@@ -99,6 +105,7 @@ A new user or contributor can:
- Install the latest development code easily (`make dev-install` or direct git+) and use `cya` for realistic tasks after reading the README.
- Understand exactly what context and memory influenced a response via `--explain-context`.
- Trust that dangerous actions will never execute without explicit confirmation.
- Start `cya shell` for multi-turn help, inspect `/explain`, export a redacted session, and opt in explicitly before shell history or session-turn memory is used.
- Use `cya retrospect` to reflect on usage and set goals that influence future behavior.
- Build and verify distribution packages locally.
- See a clear path for how real `llm-connect`, deeper `phase-memory`, and future PyPI releases will integrate.
@@ -107,7 +114,7 @@ Sibling project owners (llm-connect, phase-memory, State Hub) can read the workp
---
**This SCOPE document reflects the state after CYA-WP-0004 (Dev-Head Install & Release Packaging).**
**This SCOPE document reflects the state after CYA-WP-0007 (Interactive Shell Session) and CYA-WP-0008 (llm-connect Adapter Integration).**
It remains intentionally narrower than the long-term vision in INTENT.md and MemoryVision.md, but now incorporates significant advances in contextual memory activation, user-driven retrospection/optimization loops, and proper packaging & distribution capabilities.

View File

@@ -0,0 +1,20 @@
# CYA-WP-0006 — Profile 1 Gap Checklist
Audit of shipped Profile 1 spike (CYA-WP-0005-T05) vs MemoryVision Profile 1 acceptance criteria.
Gates implementation tasks T02T05.
| Gap | Spike state | Target (production) | Task |
|-----|-------------|---------------------|------|
| Capture UX | Single optional prompt; no preview | Guided 13 prompts (well / remember / avoid); preview + confirm; skip leaves no empty records | T02 |
| Provenance | Plain key/value only | Session date + scope in reflection provenance | T02 |
| Review / export | Manual JSON inspection | `cya memory reflections` lists/exports by scope | T03 |
| Compaction | None | Near-duplicate detection; explicit merge/replace (no silent delete) | T03 |
| Activation boost | Kind filter only | Reflections sorted ahead when recalled | T04 |
| Explain surfacing | Item count + sample keys | Reflection count + truncated lesson text + provenance | T04 |
| Response surfacing | Two reflections, 60-char truncate | Budget-capped "reflections influenced this" line | T04 |
| Observability | `by_kind` in export | Reflection counts by scope in export | T05 |
| Safety regression | Preference "never" tested | Reflections cannot downgrade destructive confirmation | T05 |
| Tests | Single spike test | Paths for capture, compaction, surfacing, safety | T05 |
| Docs | "spike" wording | README example session; MemoryVision "production" | T06 |
**Profile 0 invariants (must not regress):** user-controlled JSON, full provenance, memory adds caution only.

View File

@@ -0,0 +1,16 @@
# Example ~/.config/cya/config.toml — placeholders only; do not commit secrets.
[llm]
adapter = "connect"
backend = "openrouter"
model = "anthropic/claude-sonnet-4"
temperature = 0.3
max_tokens = 2000
api_key_env = "OPENROUTER_API_KEY"
# Optional interactive shell history context. Off by default.
# Values are capped/redacted before they enter session context.
[shell_history]
enabled = false
limit = 50
# histfile = "~/.bash_history"

View File

@@ -0,0 +1,172 @@
# cya Interactive Shell Session Design
Status: implemented for CYA-WP-0007
Date: 2026-06-23
## Scope Alignment
This design moves `cya shell` from the old deferred REPL note in SCOPE.md into
owned product scope. It preserves the core INTENT principle: `cya` helps from
inside the console while the user keeps control of context, history, memory,
backend choice, and safety decisions.
Reviewed against:
- INTENT.md: console-native, user-controlled, explainable assistance.
- SCOPE.md: `cya` owns UX, orchestration, safety, context, and documentation;
State Hub remains non-runtime coordination.
- MemoryVision.md: session turns are an episodic precursor with explicit
`kind: session_turn`, user-triggered export, and opt-in persistence to memory.
- AGENTS.md / State Hub boundary: hub reads are allowed for orientation;
hub writes require explicit operator confirmation.
## REPL UX
Command:
```bash
cya shell
cya shell --offline --no-hub
cya shell --with-history
```
Prompt: `cya> `
The shell is intentionally still a helper alongside the user's shell. It never
auto-executes commands. Natural-language turns are passed through the existing
orchestrator path: context collection, memory recall, risk classification,
mandatory confirmation for non-safe levels, adapter call, and rendering.
Slash commands:
- `/help` - show the shell command list.
- `/exit` or `/quit` - close the shell session.
- `/explain` - show the last turn's risk, context envelope, shell history
provenance, hub summary, and weakness hints.
- `/hub` - show loaded active workstreams, open tasks, inbox, and hub errors.
- `/hub log "summary"` - post a State Hub progress note only after interactive
confirmation.
- `/inbox` - show unread messages.
- `/inbox read <id>` - explicitly mark a message read.
- `/export-session` - write a redacted JSON session summary.
- `/learn` - capture Profile 1 reflections immediately.
EOF exits cleanly. Ctrl-C interrupts the current prompt and keeps the session
alive. Non-TTY input is accepted for smoke/scripted use; end-session learning
prompts are skipped unless the session is interactive.
## Session File Layout
Session artifacts are user-owned and local:
```text
~/.config/cya/sessions/<session-id>.jsonl
~/.config/cya/sessions/<session-id>-summary.json
```
Session ids use `cya-YYYYMMDD-HHMMSS-<random>`.
JSONL event kinds:
- `session_start`: session metadata, cwd, loaded history context, compact hub
orientation.
- `session_turn`: one redacted turn with user text, assistant text, risk,
cancellation/dry-run flags, weakness hints, and redaction count.
- `session_export`: emitted when `/export-session` writes a summary file.
- `session_end`: final turn count and timestamp.
`session_turn` is also registered as a memory kind. End-of-session memory writes
are opt-in and store only redacted turn summaries through the existing memory
port, not a new private store.
## Shell History Model
History is off by default.
Opt-in paths:
```bash
cya shell --with-history
```
or:
```toml
[shell_history]
enabled = true
limit = 50
histfile = "~/.bash_history"
```
Rules:
- Default hard cap: 50 lines.
- Source: `$HISTFILE`, then `.bash_history`, `.zsh_history`, or fish history.
- Per-line provenance: source path, line number, and `shell_history.histfile`.
- Redaction before session context or persistence: API keys, tokens, passwords,
common provider key forms, and secret-like env assignments.
- `/explain` shows whether history was enabled, how many lines were included,
redaction count, and provenance.
- One-shot `cya "..."` remains history-free by default; no collector invariant
changes are required for one-shot mode.
## State Hub Boundary
Session start reads:
- `.custodian-brief.md` if present.
- `GET /workstreams/?topic_id=64418556-3206-457a-ba29-6884b5b12cf3&status=active`.
- `GET /messages/?to_agent=can-you-assist&unread_only=true`.
Remote hub failures are non-fatal and are recorded in the session context.
Writes are explicit slash commands only:
- `/hub log "summary"` posts a progress note after confirmation.
- `/inbox read <id>` marks one message read by explicit command.
No automatic State Hub writes occur on shell start, normal turns, export, or
session close.
## Weakness Hints v1
Hints are deterministic informational panels. They never change the risk level,
never skip confirmation, and never execute fixes.
Rules:
- `credential-routing`: secret-like topics without `warden route` guidance.
- `ops-warden-secret-anti-pattern`: requests that imply ops-warden should vend
API keys or passwords.
- `remote-exec-review`: `curl`/`wget` piped to interpreters.
- `repeated-destructive-intent`: repeated destructive or mass-edit turns.
- `state-hub-alignment`: requested workplan id not visible in loaded hub
summary, reminding the user that local workplan files are source of truth.
## End-of-Session Learning
On interactive close, the shell offers:
1. Profile 1 lesson capture using the same reflection helpers as
`cya retrospect`.
2. Optional redacted `session_turn` memory persistence.
Skipping either path writes no reflection or session-turn memory records.
`/export-session` remains separate and always writes only a redacted JSON
summary under the sessions directory.
## Gate Checklist
- T02 REPL skeleton: `cya shell` command, prompt loop, JSONL session file,
`/help`, `/exit`, `/explain`, EOF/Ctrl-C handling.
- T03 history: off by default, `--with-history`, config support, caps,
redaction, provenance, tests.
- T04 hub: brief + active workstreams + inbox, graceful offline behavior,
`/hub`, `/hub log`, `/inbox`, explicit mark-read.
- T05 orchestrator/safety: every turn uses `handle_request`; non-safe levels
still require confirmation; `/explain` shows the full session context.
- T06 learning/export: Profile 1 capture, `/export-session`, opt-in
`session_turn` memory persistence.
- T07 hints: at least three deterministic rules with tests; advisory only.
- T08 docs/tests: README, AGENTS command reference, operator example, and
pytest coverage.

View File

@@ -0,0 +1,43 @@
# Example Operator Session
This example uses offline mode and skips remote hub calls for a local smoke.
In normal repo work, omit `--no-hub` so the shell reads State Hub orientation.
```text
$ cya shell --offline --no-hub
cya shell
Session: cya-20260623-101500-1a2b3c4d
Artifact: ~/.config/cya/sessions/cya-20260623-101500-1a2b3c4d.jsonl
State Hub remote orientation not available.
History lines: 0
Type /help for commands, /exit to finish.
cya> summarize what changed in this repo
... standard orchestrator response ...
cya> /explain
... context envelope, memory, shell history status, hub summary, and hints ...
cya> /hub
... active workstreams or offline notes ...
cya> /export-session
Exported session summary: ~/.config/cya/sessions/cya-...-summary.json
cya> /exit
Session saved: ~/.config/cya/sessions/cya-...jsonl
```
For history-aware continuity, opt in explicitly:
```bash
cya shell --with-history
```
The history context is capped, redacted, and shown in `/explain`. Secret values
should still be routed through the approved custody path, not pasted into the
session.
State Hub writes require a slash command and interactive confirmation:
```text
cya> /hub log "Implemented CYA-WP-0007 shell skeleton and tests"
Post progress note to http://127.0.0.1:8000? [y/N]: y
```

View File

@@ -0,0 +1,100 @@
# llm-connect Integration (CYA-WP-0008)
## Mapping: cya ↔ llm-connect
| cya (`AssistanceRequest`) | llm-connect |
|---------------------------|-------------|
| `user_request` | User message body (after context framing) |
| `context` (envelope + memory + `session_turns`) | Serialized into the prompt via `cya.llm.prompt.build_assistance_prompt` |
| `hints` (`model`, `temperature`, `max_tokens`) | `RunConfig` fields for `execute_prompt` |
| `AssistanceResponse.suggestion` | `LLMResponse.content` |
| `AssistanceResponse.metadata` | `LLMResponse.model`, `usage`, `finish_reason` |
llm-connect owns provider clients (`create_adapter`), API key resolution, retries, and
token usage. `cya` never imports vendor SDKs directly.
## Configuration
User config: `~/.config/cya/config.toml`
```toml
[llm]
adapter = "connect" # "connect" | "fake" (default: fake when absent)
backend = "openrouter" # openrouter | openai | gemini | claude-code | mock
model = "anthropic/claude-sonnet-4"
temperature = 0.3
max_tokens = 2000
api_key_env = "OPENROUTER_API_KEY" # optional override
# system_prompt = "..." # optional; uses cya default when omitted
```
Optional project override: `.cya.toml` (same `[llm]` section; merged over user config).
Environment overrides:
| Variable | Purpose |
|----------|---------|
| `CYA_LLM_ADAPTER` | `connect` or `fake` |
| `CYA_LLM_BACKEND` / `CYA_LLM_PROVIDER` | Provider name |
| `CYA_LLM_MODEL` | Model id |
CLI: `cya --offline "..."` forces `FakeLLMAdapter`.
## Session context budget (multi-turn / `cya shell`)
Recent turns are passed in `AssistanceRequest.context["session_turns"]` as
`{"user": "...", "assistant": "..."}` records.
Bounds (see `cya.config`):
- **Max turns:** 10
- **Max characters:** 4000 (total across included turns)
Older or oversized history is dropped from the prompt automatically.
## Credential routing
Do **not** commit API keys. Before requesting secrets, route custody:
```bash
warden route find "OpenRouter API key" --json
warden route show <catalog-id> --json
```
Typical ownership:
| Need | Owner | ops-warden executes? |
|------|-------|----------------------|
| `OPENROUTER_API_KEY` | OpenBao (`railiance-platform`) | No — route only |
| `OPENAI_API_KEY` | OpenBao | No — route only |
| `GEMINI_API_KEY` | OpenBao | No — route only |
llm-connect resolves keys via `resolve_api_key()` (explicit arg → env var → project key file).
## Adapter selection
`cya.llm.factory.get_adapter()` is the single factory for one-shot and shell paths:
1. `--offline` or `CYA_LLM_ADAPTER=fake``FakeLLMAdapter`
2. `adapter = "connect"` in config/env → `LLMConnectAdapter` (graceful degrade on failure)
3. Otherwise → `FakeLLMAdapter` (current default)
## Installation
```bash
make dev-install
pip install -e ~/llm-connect # sibling checkout
```
Optional extra group (placeholder for packaging): `pip install -e ".[llm]"`.
## Tests
- Default CI: `make test` — mocks llm-connect; no network.
- Manual live check: `pytest -m llm_live` (requires configured API key).
## Known gaps
- Structured JSON output schema not enforced yet (free-form model text).
- `claude-code` backend does not require an API key; other backends do.
- Per-directory `.cya.toml` overrides user config but does not yet mirror llm-connect's full 7-layer resolution.

View File

@@ -1,10 +1,12 @@
# Optimization Suggestions & Missing Functionality for phase-memory
**Date:** 2026-05-28
**Date:** 2026-05-28 (finalized in CYA-WP-0005 T04)
**Source:** CYA-WP-0005 (Agentic Memory Profiles) + persisted research `history/2026-05-28-CYA-Agentic-Memory-Research-Variations.md`
**Audience:** phase-memory / markitect owners (sister repo)
**Purpose:** Actionable, prioritized feedback from the cya consumer so that Profiles 03 (and the self-improving loops they enable) can be realized cleanly, safely, and with excellent user control / explainability.
**Updated after T03:** Cross-references added to the crisp profile definitions and Capability Matrix now living in MemoryVision.md. The 9 categories below remain the concrete interface asks needed to enable the three variations.
## Context (Why This Feedback Exists)
`can-you-assist` (`cya`) is a **consumer** of the phase-memory profile-driven memory layer. After CYA-WP-0002/0003 we have a high-quality local approximation (Profile 0) behind explicit ports that already uses `profile`, `kinds`, `activation_context`, retrospection outcomes as higher-order memory, provenance, and phase hints. This delivers real directory/project-bound activation and a `cya retrospect` continuous-optimization loop today.
@@ -26,6 +28,8 @@ All suggestions respect cya invariants:
## Prioritized Suggestions (9 Categories)
See the "Profiles 13: Definitions and cya Integration Plans" section + Capability Matrix in MemoryVision.md for the detailed intent and cya mappings that these interface asks are designed to enable.
### 1. Refined Port Signatures & Activation Context (Must-have for Profile 1, baseline for 2/3)
- Make `activation_context: dict[str, Any]` (cwd, git_root, task_class, recent_kinds, token_budget_hint) a first-class, documented parameter on `recall_preferences` / context-package requests (cya already prototypes this locally).
- Support `kinds: list[str]` filter + boost natively (cya has `KIND_PREFERENCE | RETROSPECTION | INTERACTION_GOAL`; will add `REFLECTION`, `SYNTHESIZED_CONVENTION`, `PROCEDURAL_RULE`, etc.).

View File

@@ -0,0 +1,129 @@
# Gap Analysis: INTENT.md vs SCOPE.md (Post CYA-WP-0005)
**Date:** 2026-05-28
**Repo:** can-you-assist
**Workplan:** CYA-WP-0005 (completed)
**Previous Analysis:** 2026-05-28 (Post 0004)
**Author:** Grok
## Executive Summary
Since the May 28 analysis (post-0004), the major workplan **CYA-WP-0005: Agentic Memory Profiles and phase-memory Interface Optimization** has been completed via ralph loop.
Key deliveries:
- Formal **Profile 0 baseline** (post-0003 local JSON + kinds + activation_context + provenance + retrospection) explicitly documented as the stable foundation.
- Complete profile model: detailed definitions, cya integration plans, and a Capability Matrix for **Profiles 13** (Reflexion verbal, hierarchical synthesis, procedural/meta-policy evolution).
- First delivered self-improving capability: **minimal but working Profile 1** (Reflexion-style verbal reflections/lessons) spike — `remember_reflection()` + `KIND_REFLECTION`, optional "capture verbal lesson" step inside `cya retrospect`, preferential activation, and surfacing in responses / `--explain-context`.
- Finalized the `docs/phase-memory-optimization-suggestions.md` handoff artifact for the sister repo.
**Overall Assessment**: Very strong progress on the most important remaining gap from INTENT.md — the "Personalized Console Helper" that improves over time through user-controlled memory. The memory & longitudinal adaptation gap has moved from "Small-Medium" to "Small". The project now has both the conceptual framework (profiles) and a concrete first implementation (Profile 1 verbal loop) that directly addresses INTENT's vision of longitudinal adaptation via user-steerable memory.
Remaining gaps are now primarily about *depth* (richer context understanding, deeper phase-memory integration for Profiles 2/3, procedural evolution) rather than missing foundational mechanisms.
## Strong Alignments (Updated)
| Area | INTENT.md Position | Current Reality (Post-0005) | Assessment |
|-----------------------------------|--------------------------------------------------|---------------------------------------------------------------------------------------------|-------------------------|
| Console-native experience | Foundational | Excellent | Strong match |
| Safety & mandatory confirmation | Important | Core behavior + memory-aware (reflections can force confirmation) | Exceeded |
| Explainability & transparency | Strong requirement | Very strong (provenance everywhere, reflections surfaced explicitly, `--explain-context`) | Strong |
| Backend agnosticism | Must use llm-connect seam | Clean LLMAdapter Protocol + Fake | Excellent |
| User-controlled memory | Central principle | Real + now architected with explicit profiles (0 baseline + 13 roadmap) | Major improvement |
| Longitudinal adaptation | Personalized helper that improves over time | `cya retrospect` + automatic activation + first working self-improvement loop (Profile 1) | Significantly better |
| Packaging & distribution | Not heavily emphasized | Dev-head install + release process first-class | Major positive shift |
| Clear boundaries | cya / llm-connect / phase-memory separation | Explicit ports + MemoryVision contract + phase-memory feedback doc | Good |
| Profile-driven / self-improving behavior | Strong vision in "Personalized Console Helper" | Full model (T03) + first implementation (T05) | New major strength |
## Key Gaps (Post-0005)
### 1. Memory & Longitudinal Adaptation — Now a Clear Strength with Clear Next Steps
**Previous Status (Post-0004)**: Small-Medium. Good activation + retrospection loop, but still passive local JSON with limited self-improvement.
**Current Status (Post-0005)**:
- Explicit **Profile 0** baseline documented as the shipped foundation.
- Complete **Profiles 13** model with cya-specific mappings and Capability Matrix (T03).
- **Profile 1** (Reflexion verbal) is not just designed — a working spike is shipped: verbal lessons can be captured during `cya retrospect`, stored with dedicated kind, preferentially activated, and surfaced to the user.
- Strong explainability and user control preserved.
**Remaining Sub-Gaps** (now much smaller and more targeted):
- Profile 1 is still a minimal spike (capture step + basic activation). Production hardening, better UX, and compaction of reflections would be natural follow-on work.
- Profiles 2 (hierarchical synthesis) and 3 (procedural/meta-policy) remain design + research artifacts. No implementation yet (explicitly registered as debt in 0005).
- Full wiring to `phase-memory` planners, graph store, and lifecycle rules is still future work (as intended).
**Assessment**: Large positive movement. The core INTENT vision of a user-controlled assistant that "improves over time" now has both architecture and a concrete first capability. This was the single largest gap for most of the project's history.
### 2. Depth of Local Context Understanding
**INTENT.md** envisions rich assistance with code repositories, notes, project structures, and conventions.
**Current State**:
- Context collector remains intentionally shallow (top-level entries + basic git).
- Memory (especially the new Profile 1 verbal lessons + user-declared retrospection goals) helps significantly with *user-taught* project conventions and patterns.
- No automatic deep semantic understanding, embeddings, or large-scale repository analysis.
**Assessment**: Still a medium gap. The memory/profile work makes the *user* far more effective at bringing rich context into the assistant, but the system itself does not yet deeply understand repositories or notes on its own. This is consistent with original scope boundaries.
### 3. One-Shot vs Rich Longitudinal Value
**INTENT.md**: Strong vision of the assistant becoming more useful over time through memory of habits, conventions, and recurring workflows.
**Current Reality (Post-0005)**:
- Users can teach the system via normal use + explicit `cya retrospect`.
- Directory/project-bound activation + the new verbal reflection mechanism create a real, usable self-improvement loop.
- Profile model provides a clear roadmap for richer future behavior (synthesis, procedural rules).
**Assessment**: Significantly improved. The basic mechanism for longitudinal, user-controlled adaptation now exists and is demonstrable with Profile 1. Richer automatic pattern learning and procedural evolution (Profiles 2/3) remain the natural next deepening.
### 4. Packaging & Distribution — Remains Strong
No regression. Still first-class with documented process.
### 5. Safety & Explainability — Continued Strength
Remains excellent. The new Profile 1 reflections flow through the same rule-based RiskClassifier and are fully visible in `--explain-context`.
## Summary Table (Updated)
| Gap Area | Severity (Post-0002) | Severity (Post-0004) | Severity (Post-0005) | Trend | Notes |
|-----------------------------------|----------------------|----------------------|----------------------|------------------------|-------|
| Memory & Longitudinal Adaptation | Medium | Small-Medium | Small | Major improvement | Profile model + first working self-improvement loop (Profile 1) |
| Depth of Context Understanding | Medium-Large | Medium | Medium | Stable / slight improvement via user memory | Collector still shallow; user memory helps |
| Packaging & Distribution | Medium (gap) | Small | Small | Stable | Remains first-class |
| Safety & Explainability | Positive | Positive | Positive | Stable | Remains a core strength |
| One-shot vs Rich Longitudinal | Medium | Small | Small | Stable / improved | Real loop exists; richer synthesis is future work |
## Recommendations
1. **Continue the memory evolution roadmap** as documented in CYA-WP-0005 and MemoryVision.md:
- Production-harden Profile 1 (better capture UX, reflection compaction, more surfacing).
- Begin exploration spikes for Profile 2 (synthesis) when ready.
- Treat Profile 3 (procedural) as higher-risk / higher-reward and keep strong guardrails.
2. **Deepen phase-memory coordination** using the finalized `docs/phase-memory-optimization-suggestions.md`. The 9 categories (especially synthesis planner hooks, procedural support, and dry-run proposal semantics) are now well-scoped asks.
3. **Consider whether "Context Depth" becomes a first-class work item**. If richer automatic understanding of repositories/notes is desired, it will likely need its own focused slice (separate from the memory/profile work).
4. **Keep the seam discipline**. The explicit ports, activation model, and safety integration from 0002/0003/0005 remain excellent boundaries. Future work should extend rather than bypass them.
5. **Packaging follow-through** (unchanged from prior analysis): move the registered debt items (CI gate, PyPI workflow, automation) forward when capacity allows.
## Conclusion
CYA-WP-0005 has delivered the single most important conceptual and practical advance since the memory integration work of 0002/0003. The project now has:
- An explicit, documented profile model that directly maps to INTENT's vision of longitudinal adaptation.
- A working, user-visible first self-improving capability (Profile 1 verbal reflections).
The product is now substantially closer to the "Personalized Console Helper" described in INTENT.md than at any previous point. The remaining gaps are about *evolving and deepening* capabilities that now have clear architectural homes, rather than about missing foundational pieces.
This is a strong position from which to plan the next phase of work.
---
**Related Documents**
- Previous analysis: `history/2026-05-28-CYA-Intent-Scope-Gap-Analysis-Post-0004.md`
- Research foundation: `history/2026-05-28-CYA-Agentic-Memory-Research-Variations.md`
- Memory vision & profile model: `MemoryVision.md`
- Recent workplan: `workplans/CYA-WP-0005-agentic-memory-profiles-and-phase-memory-feedback.md`
- Current SCOPE.md (updated as part of this assessment)
- phase-memory feedback: `docs/phase-memory-optimization-suggestions.md`

View File

@@ -15,6 +15,7 @@ authors = [
dependencies = [
"typer[standard]>=0.12.0",
"rich>=13.0.0",
"tomli>=2.0.0; python_version<'3.11'",
]
[project.optional-dependencies]
@@ -26,6 +27,9 @@ dev = [
test = [
"pytest>=8.0",
]
llm = [
# Install llm-connect from a sibling checkout, e.g. pip install -e ~/llm-connect
]
[project.scripts]
cya = "cya.cli.main:run"
@@ -57,6 +61,7 @@ python_functions = ["test_*"]
addopts = "-q --tb=short"
markers = [
"safety: core safety and risk classifier invariants (always run)",
"llm_live: live llm-connect inference (requires API key; manual runs only)",
]
[tool.setuptools_scm]

12
registry/README.md Normal file
View File

@@ -0,0 +1,12 @@
# Capability Registry
Markdown-first capability index for federation and reuse planning.
## Authoring
1. Copy a capability entry template (see reuse-surface `templates/capability-entry.template.md`).
2. Add the row to `indexes/capabilities.yaml`.
3. Run `reuse-surface validate` from a checkout with the CLI installed.
4. Merge to `main` and verify publish with `reuse-surface establish --publish-check`.
Federation contract: reuse-surface `docs/RegistryFederation.md`.

View File

View File

@@ -0,0 +1,120 @@
---
id: capability.agents.cli-assistant
name: Console-Native LLM Assistant (cya)
summary: Console-native, backend-agnostic assistant CLI that expresses user intent in natural language
and returns safe, explainable, context-aware help.
owner: can-you-assist
status: draft
domain: agents
tags:
- cli
- assistant
- llm
- safety
maturity:
discovery:
current: D2
target: D4
confidence: medium
rationale: README documents the MVP slice (CYA-WP-0001), the LLMAdapter seam, and the rule-based risk-classification
safety layer; no separate INTENT/SCOPE file found.
availability:
current: A2
target: A3
confidence: medium
rationale: Installable today via `pip install -e .`; ships a working CLI (`cya "..."`, `--explain-context`)
with a FakeLLMAdapter default and optional real llm-connect backend.
external_evidence:
completeness:
level: C1
confidence: low
basis: scope_vs_intent_and_consumer_expectations
satisfied_expectations:
- natural-language CLI entry point
- context-explain mode
- mandatory confirmation for destructive/privileged/mass-edit/network actions
broken_expectations: []
out_of_scope_expectations: []
reliability:
level: R0
confidence: low
basis: consumer_quality_signals
known_reliability_risks:
- MVP slice only; real llm-connect backend optional/not always configured
discovery:
intent: Let users express intent in natural language from the terminal and receive safe, explainable,
context-aware help, without owning LLM inference or memory storage itself.
includes:
- CLI orchestration and UX
- rule-based risk classification and confirmation gating
- LLMAdapter seam to llm-connect
excludes:
- LLM inference (owned by llm-connect)
- memory storage (owned by phase-memory)
assumptions: []
use_cases: []
research_memos: []
availability:
current_level: A2
target_level: A3
current_artifacts:
- '`cya` CLI package, pip-installable'
target_artifacts: []
consumption_modes:
- cli
relations:
depends_on: []
supports: []
related_to: []
evidence:
documentation:
- README.md
tests:
- tests/
consumer_feedback: []
bug_reports: []
incidents: []
consumer_guidance:
recommended_for:
- shell-based workflows wanting a safety-gated natural-language assistant
not_recommended_for:
- needs beyond the first MVP slice (CYA-WP-0001)
known_limitations:
- first narrow MVP slice; FakeLLMAdapter is the default, real backend is optional
promotion_history: []
---
# Console-Native LLM Assistant (cya)
## Overview
`cya` is the CLI surface for the capabilities domain: it owns orchestration, UX, and a safety layer, and talks to `llm-connect` only through a stable `LLMAdapter` boundary, keeping memory under `phase-memory`.
## Assessment notes
### Discovery
README documents the MVP slice (CYA-WP-0001), the LLMAdapter seam, and the rule-based risk-classification safety layer; no separate INTENT/SCOPE file found.
### Availability
Installable today via `pip install -e .`; ships a working CLI (`cya "..."`, `--explain-context`) with a FakeLLMAdapter default and optional real llm-connect backend.
### Completeness
First-pass honest assessment from the REUSE-WP-0017 coverage campaign
(reuse-surface). No external consumer feedback exists yet; levels reflect
scope-vs-intent documentation quality, not internal code quality.
### Reliability
No production consumer telemetry exists yet; reliability level is
intentionally conservative pending REUSE-WP-0019 reuse-telemetry evidence.
## Promotion checklist
- [x] ID follows `capability.<domain>.<name>` pattern
- [x] Maturity enums match `specs/CapabilityMaturityStandard.md`
- [x] `external_evidence` is populated separately from `maturity`
- [ ] Relations reference valid capability IDs (none yet)
- [x] Index entry added in `registry/indexes/capabilities.yaml`

View File

@@ -0,0 +1,20 @@
version: 1
updated: '2026-07-06'
domain: helix_forge
capabilities:
- id: capability.agents.cli-assistant
name: Console-Native LLM Assistant (cya)
summary: Console-native, backend-agnostic assistant CLI that expresses user intent in natural language
and returns safe, explainable, context-aware help.
vector: D2 / A2 / C1 / R0
domain: agents
status: draft
owner: can-you-assist
path: registry/capabilities/capability.agents.cli-assistant.md
tags:
- cli
- assistant
- llm
- safety
consumption_modes:
- cli

View File

@@ -11,6 +11,7 @@ This module will evolve in T06 (orchestrator) but the surface contract stays sta
from __future__ import annotations
import json
import sys
import typer
@@ -65,6 +66,11 @@ def main(
"-n",
help="Preview mode — do not perform any actions (stub in T01).",
),
offline: bool = typer.Option(
False,
"--offline",
help="Use the deterministic FakeLLMAdapter (no llm-connect / no API keys).",
),
version: bool = typer.Option(
None,
"--version",
@@ -105,9 +111,111 @@ def main(
request,
explain_context=explain_context,
dry_run=dry_run,
offline=offline,
)
@app.command("shell")
def shell_command(
with_history: bool = typer.Option(
False,
"--with-history",
help="Opt in to capped, redacted shell-history context for this session.",
),
history_limit: int | None = typer.Option(
None,
"--history-limit",
help="Maximum history lines to include, capped at the configured safe limit.",
),
offline: bool = typer.Option(
False,
"--offline",
help="Use the deterministic FakeLLMAdapter for this shell session.",
),
hub_url: str = typer.Option(
"http://127.0.0.1:8000",
"--hub-url",
help="State Hub base URL for read-only session orientation.",
),
no_hub: bool = typer.Option(
False,
"--no-hub",
help="Skip State Hub HTTP orientation; still reads .custodian-brief.md if present.",
),
offer_session_lessons: bool = typer.Option(
True,
"--session-lessons/--no-session-lessons",
help="Offer end-of-session Profile 1 reflection and session_turn memory capture.",
),
) -> None:
"""Start an interactive, stateful cya shell session."""
from cya.shell_session import run_shell
run_shell(
with_history=with_history,
history_limit=history_limit,
offline=offline,
hub_url=hub_url,
no_hub=no_hub,
offer_end_learning=offer_session_lessons,
)
memory_app = typer.Typer(
help="Inspect and manage user-controlled memory (Profile 0 + Profile 1).",
rich_markup_mode="rich",
)
app.add_typer(memory_app, name="memory")
@memory_app.command("reflections")
def memory_reflections(
scope: str = typer.Option(
".",
"--scope",
"-s",
help="Directory or scope to list reflections for.",
),
export_json: bool = typer.Option(
False,
"--json",
help="Export reflections as JSON (same as export_memory with kinds filter).",
),
) -> None:
"""List or export verbal reflections for a scope (Profile 1)."""
from cya.memory import export_memory, KIND_REFLECTION
from cya.memory.reflections import list_reflections, reflection_export_stats
if export_json:
data = export_memory(scope, kinds=[KIND_REFLECTION])
console.print(json.dumps(data, indent=2, default=str))
return
items = list_reflections(scope)
stats = reflection_export_stats(scope)
if not items:
console.print(f"[yellow]No reflections in scope {scope!r}.[/yellow]")
return
lines = []
for item in items:
prov = item.get("provenance") or {}
date = prov.get("session_date", "?")
lines.append(f"• [bold]{item.get('key')}[/bold] ({date}): {item.get('value')}")
console.print(
Panel(
"\n".join(lines),
title=f"Reflections in {scope} ({stats['reflection_count']} total)",
border_style="cyan",
)
)
if stats.get("reflection_counts_by_scope"):
console.print(
f"[dim]By scope: {stats['reflection_counts_by_scope']}[/dim]"
)
@app.command()
def retrospect(
scope: str = typer.Option(
@@ -133,8 +241,136 @@ def retrospect(
run_retrospection(scope=scope, limit=limit)
if __name__ == "__main__":
app()
def _dispatch_shell_argv(argv: list[str] | None = None) -> bool:
"""Handle `cya shell ...` before Typer's root REQUEST argument sees it."""
import argparse
argv = list(sys.argv if argv is None else argv)
if len(argv) < 2 or argv[1] != "shell":
return False
parser = argparse.ArgumentParser(
prog=f"{argv[0]} shell",
description="Start an interactive, stateful cya shell session.",
)
parser.add_argument(
"--with-history",
action="store_true",
help="Opt in to capped, redacted shell-history context for this session.",
)
parser.add_argument(
"--history-limit",
type=int,
default=None,
help="Maximum history lines to include, capped at the configured safe limit.",
)
parser.add_argument(
"--offline",
action="store_true",
help="Use the deterministic FakeLLMAdapter for this shell session.",
)
parser.add_argument(
"--hub-url",
default="http://127.0.0.1:8000",
help="State Hub base URL for read-only session orientation.",
)
parser.add_argument(
"--no-hub",
action="store_true",
help="Skip State Hub HTTP orientation; still reads .custodian-brief.md if present.",
)
parser.add_argument(
"--session-lessons",
dest="offer_session_lessons",
action="store_true",
default=True,
help="Offer end-of-session Profile 1 reflection and session_turn memory capture.",
)
parser.add_argument(
"--no-session-lessons",
dest="offer_session_lessons",
action="store_false",
help="Skip end-of-session learning prompts.",
)
args = parser.parse_args(argv[2:])
from cya.shell_session import run_shell
run_shell(
with_history=args.with_history,
history_limit=args.history_limit,
offline=args.offline,
hub_url=args.hub_url,
no_hub=args.no_hub,
offer_end_learning=args.offer_session_lessons,
)
return True
def _dispatch_memory_argv(argv: list[str] | None = None) -> bool:
"""Handle `cya memory ...` before the root REQUEST argument sees it."""
import argparse
argv = list(sys.argv if argv is None else argv)
if len(argv) < 2 or argv[1] != "memory":
return False
parser = argparse.ArgumentParser(
prog=f"{argv[0]} memory",
description="Inspect and manage user-controlled memory.",
)
sub = parser.add_subparsers(dest="command", required=True)
reflections = sub.add_parser("reflections", help="List or export Profile 1 reflections.")
reflections.add_argument(
"--scope",
"-s",
default=".",
help="Directory or scope to list reflections for.",
)
reflections.add_argument(
"--json",
dest="export_json",
action="store_true",
help="Export reflections as JSON.",
)
args = parser.parse_args(argv[2:])
if args.command == "reflections":
memory_reflections(scope=args.scope, export_json=args.export_json)
return True
return False
def _dispatch_retrospect_argv(argv: list[str] | None = None) -> bool:
"""Handle `cya retrospect ...` before the root REQUEST argument sees it."""
import argparse
argv = list(sys.argv if argv is None else argv)
if len(argv) < 2 or argv[1] != "retrospect":
return False
parser = argparse.ArgumentParser(
prog=f"{argv[0]} retrospect",
description="Start a guided retrospection session.",
)
parser.add_argument(
"--scope",
"-s",
default=".",
help="Directory or scope to run retrospection on.",
)
parser.add_argument(
"--limit",
type=int,
default=8,
help="Number of recent memory items to review.",
)
args = parser.parse_args(argv[2:])
from cya.orchestrator import run_retrospection
run_retrospection(scope=args.scope, limit=args.limit)
return True
def run() -> None:
@@ -144,4 +380,10 @@ def run() -> None:
Using a thin wrapper around app() lets us keep the full-featured
@app.callback(invoke_without_command=True) + ctx signature for Typer/Click.
"""
if _dispatch_shell_argv() or _dispatch_memory_argv() or _dispatch_retrospect_argv():
return
app()
if __name__ == "__main__":
run()

263
src/cya/config.py Normal file
View File

@@ -0,0 +1,263 @@
"""User configuration for cya (CYA-WP-0008-T03).
Reads ``~/.config/cya/config.toml`` and optional project ``.cya.toml``.
Environment variables override file values where noted.
"""
from __future__ import annotations
import os
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
_USER_CONFIG = Path.home() / ".config" / "cya" / "config.toml"
_PROJECT_CONFIG_NAME = ".cya.toml"
# Session context bounds (CYA-WP-0008-T04) — documented in docs/llm-connect-integration.md
MAX_SESSION_TURNS = 10
MAX_SESSION_CHARS = 4000
DEFAULT_SHELL_HISTORY_LINES = 50
def _load_toml(path: Path) -> dict[str, Any]:
if not path.is_file():
return {}
if sys.version_info >= (3, 11):
import tomllib
return tomllib.loads(path.read_text())
import tomli
return tomli.loads(path.read_bytes())
def _find_project_config(start: Path | None = None) -> Path | None:
current = (start or Path.cwd()).resolve()
for directory in [current, *current.parents]:
candidate = directory / _PROJECT_CONFIG_NAME
if candidate.is_file():
return candidate
return None
def _merge_llm_sections(*sources: dict[str, Any]) -> dict[str, Any]:
merged: dict[str, Any] = {}
for source in sources:
section = source.get("llm")
if isinstance(section, dict):
merged.update(section)
return merged
def _merge_shell_history_sections(*sources: dict[str, Any]) -> dict[str, Any]:
merged: dict[str, Any] = {}
for source in sources:
section = source.get("shell_history")
if isinstance(section, dict):
merged.update(section)
return merged
@dataclass
class LLMSettings:
"""Resolved LLM adapter settings."""
adapter: str = "fake" # "fake" | "connect"
backend: str = "openrouter"
model: str | None = None
temperature: float = 0.3
max_tokens: int = 2000
api_key_env: str | None = None
system_prompt: str | None = None
configured: bool = False
source: str = "default"
def to_hints(self) -> dict[str, Any]:
hints: dict[str, Any] = {
"backend": self.backend,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
}
if self.model:
hints["model"] = self.model
if self.api_key_env:
hints["api_key_env"] = self.api_key_env
return hints
@dataclass
class ShellHistorySettings:
"""Resolved shell-history collection settings.
History is intentionally off unless enabled by the CLI or config. The line
limit is capped so a session cannot accidentally ingest an unbounded shell
history file.
"""
enabled: bool = False
limit: int = DEFAULT_SHELL_HISTORY_LINES
histfile: str | None = None
source: str = "default"
requested_limit: int | None = None
notes: list[str] = field(default_factory=list)
def _coerce_float(value: Any, default: float) -> float:
try:
return float(value)
except (TypeError, ValueError):
return default
def _coerce_int(value: Any, default: int) -> int:
try:
return int(value)
except (TypeError, ValueError):
return default
def _coerce_bool(value: Any, default: bool = False) -> bool:
if isinstance(value, bool):
return value
if value is None:
return default
text = str(value).strip().lower()
if text in {"1", "true", "yes", "y", "on"}:
return True
if text in {"0", "false", "no", "n", "off"}:
return False
return default
def load_llm_settings(*, offline: bool = False) -> LLMSettings:
"""Resolve LLM settings from env, user config, and project config."""
if offline:
return LLMSettings(adapter="fake", configured=False, source="--offline")
env_adapter = os.environ.get("CYA_LLM_ADAPTER", "").strip().lower()
if env_adapter in ("fake", "connect"):
base = LLMSettings(adapter=env_adapter, configured=env_adapter == "connect", source="CYA_LLM_ADAPTER")
else:
base = LLMSettings()
user_data = _load_toml(_USER_CONFIG)
project_path = _find_project_config()
project_data = _load_toml(project_path) if project_path else {}
merged = _merge_llm_sections(user_data, project_data)
if merged:
file_adapter = str(merged.get("adapter", "")).strip().lower()
if file_adapter in ("fake", "connect") and not env_adapter:
base.adapter = file_adapter
base.configured = file_adapter == "connect"
base.source = str(project_path or _USER_CONFIG)
backend = merged.get("backend") or merged.get("provider")
if backend:
base.backend = str(backend)
if not env_adapter and file_adapter != "fake":
base.adapter = "connect"
base.configured = True
base.source = str(project_path or _USER_CONFIG)
if merged.get("model"):
base.model = str(merged["model"])
base.temperature = _coerce_float(merged.get("temperature"), base.temperature)
base.max_tokens = _coerce_int(merged.get("max_tokens"), base.max_tokens)
if merged.get("api_key_env"):
base.api_key_env = str(merged["api_key_env"])
if merged.get("system_prompt"):
base.system_prompt = str(merged["system_prompt"])
env_backend = os.environ.get("CYA_LLM_BACKEND") or os.environ.get("CYA_LLM_PROVIDER")
if env_backend:
base.backend = env_backend.strip()
if base.adapter != "fake":
base.adapter = "connect"
base.configured = True
base.source = "CYA_LLM_BACKEND"
env_model = os.environ.get("CYA_LLM_MODEL")
if env_model:
base.model = env_model.strip()
if base.adapter != "fake":
base.adapter = "connect"
base.configured = True
base.source = "CYA_LLM_MODEL"
return base
def load_shell_history_settings(
*,
with_history: bool = False,
history_limit: int | None = None,
) -> ShellHistorySettings:
"""Resolve optional shell-history collection settings.
The default is always disabled. Config can enable history with:
``[shell_history] enabled = true``
The CLI ``--with-history`` flag also enables it for just that shell session.
"""
user_data = _load_toml(_USER_CONFIG)
project_path = _find_project_config()
project_data = _load_toml(project_path) if project_path else {}
merged = _merge_shell_history_sections(user_data, project_data)
settings = ShellHistorySettings()
if merged:
source = str(project_path or _USER_CONFIG)
settings.enabled = _coerce_bool(merged.get("enabled"), False)
settings.limit = _coerce_int(merged.get("limit"), settings.limit)
settings.histfile = str(merged["histfile"]) if merged.get("histfile") else None
settings.source = source
if with_history:
settings.enabled = True
settings.source = "--with-history"
if history_limit is not None:
settings.requested_limit = history_limit
settings.limit = _coerce_int(history_limit, settings.limit)
if with_history:
settings.source = "--with-history"
if settings.limit < 0:
settings.notes.append("Negative history limit was treated as 0.")
settings.limit = 0
if settings.limit > DEFAULT_SHELL_HISTORY_LINES:
settings.notes.append(
f"History limit capped at {DEFAULT_SHELL_HISTORY_LINES} lines."
)
settings.limit = DEFAULT_SHELL_HISTORY_LINES
return settings
def bound_session_turns(
turns: list[dict[str, str]] | None,
*,
max_turns: int = MAX_SESSION_TURNS,
max_chars: int = MAX_SESSION_CHARS,
) -> list[dict[str, str]]:
"""Trim session history to a bounded token/line budget for the adapter."""
if not turns:
return []
recent = turns[-max_turns:]
bounded: list[dict[str, str]] = []
used = 0
for turn in recent:
user = turn.get("user", "")
assistant = turn.get("assistant", "")
chunk_len = len(user) + len(assistant)
if used + chunk_len > max_chars and bounded:
break
bounded.append({"user": user, "assistant": assistant})
used += chunk_len
return bounded

View File

@@ -17,11 +17,15 @@ from .adapter import (
LLMAdapter,
FakeLLMAdapter,
)
from .connect_adapter import LLMConnectAdapter
from .factory import get_adapter
__all__ = [
"AssistanceRequest",
"AssistanceResponse",
"LLMAdapter",
"FakeLLMAdapter",
"LLMConnectAdapter",
"get_adapter",
]

View File

@@ -0,0 +1,138 @@
"""llm-connect-backed adapter (CYA-WP-0008-T02)."""
from __future__ import annotations
from typing import Any
from cya.config import LLMSettings
from cya.llm.adapter import AssistanceRequest, AssistanceResponse
from cya.llm.prompt import build_assistance_prompt
_PROVIDER_ENV_KEYS: dict[str, str] = {
"openrouter": "OPENROUTER_API_KEY",
"openai": "OPENAI_API_KEY",
"gemini": "GEMINI_API_KEY",
}
class LLMConnectAdapter:
"""Delegates to llm-connect while satisfying cya's LLMAdapter protocol."""
def __init__(self, settings: LLMSettings) -> None:
self._settings = settings
self._client: Any | None = None
self._init_error: str | None = None
self._ensure_client()
def _ensure_client(self) -> None:
try:
from llm_connect import create_adapter
from llm_connect.config import resolve_api_key
except ImportError:
self._init_error = (
"llm-connect is not installed. Install with:\n"
" pip install -e ~/llm-connect\n"
"or: pip install -e \".[llm]\" after adding llm-connect to your environment."
)
return
env_var = self._settings.api_key_env or _PROVIDER_ENV_KEYS.get(
self._settings.backend, "OPENROUTER_API_KEY"
)
api_key = resolve_api_key(env_var=env_var)
if self._settings.backend in ("openrouter", "openai", "gemini") and not api_key:
self._init_error = (
f"No API key found for backend {self._settings.backend!r} "
f"(checked env {env_var!r}).\n"
"Route credential custody via warden before requesting secrets:\n"
" warden route find \"OpenRouter API key\" --json\n"
"Then export the key into your environment — never commit it to the repo."
)
return
try:
self._client = create_adapter(
provider=self._settings.backend,
model=self._settings.model,
api_key=api_key,
system_prompt=self._settings.system_prompt,
)
except Exception as exc: # noqa: BLE001 — surface config errors to the user
self._init_error = f"Failed to initialize llm-connect adapter: {exc}"
def complete(self, request: AssistanceRequest) -> AssistanceResponse:
if self._init_error or self._client is None:
return self._degraded_response(self._init_error or "llm-connect client unavailable.")
try:
from llm_connect.models import RunConfig
except ImportError:
return self._degraded_response(self._init_error or "llm-connect not installed.")
system, user_prompt = build_assistance_prompt(
request,
system_prompt=self._settings.system_prompt,
)
hints = {**self._settings.to_hints(), **request.hints}
run_config = RunConfig(
model_name=hints.get("model") or self._settings.model or "anthropic/claude-sonnet-4",
temperature=float(hints.get("temperature", self._settings.temperature)),
max_tokens=int(hints.get("max_tokens", self._settings.max_tokens)),
)
# Re-create adapter when per-request system prompt differs (llm-connect stores it at init).
client = self._client
if system and not self._settings.system_prompt:
from llm_connect import create_adapter
from llm_connect.config import resolve_api_key
env_var = self._settings.api_key_env or _PROVIDER_ENV_KEYS.get(
self._settings.backend, "OPENROUTER_API_KEY"
)
api_key = resolve_api_key(env_var=env_var)
client = create_adapter(
provider=self._settings.backend,
model=self._settings.model,
api_key=api_key,
system_prompt=system,
)
try:
llm_response = client.execute_prompt(user_prompt, run_config)
except Exception as exc: # noqa: BLE001 — user-facing degrade path
return self._degraded_response(
f"llm-connect request failed: {exc}",
partial_raw=str(exc),
)
content = (llm_response.content or "").strip()
return AssistanceResponse(
suggestion=content or "(empty model response)",
explanation="Response generated via llm-connect.",
rationale="Model inference using configured backend and bounded local context.",
risks=[],
raw_model_output=content,
metadata={
"adapter": "LLMConnectAdapter",
"backend": self._settings.backend,
"model": llm_response.model,
"usage": llm_response.usage,
"finish_reason": llm_response.finish_reason,
},
)
@staticmethod
def _degraded_response(message: str, *, partial_raw: str | None = None) -> AssistanceResponse:
return AssistanceResponse(
suggestion=(
"cya could not reach a configured LLM backend.\n\n"
f"{message}\n\n"
"Continuing in offline mode: re-run with `--offline` or configure "
"`~/.config/cya/config.toml` (see README)."
),
explanation="Graceful degradation — no live inference was performed.",
rationale="llm-connect unavailable or misconfigured.",
risks=["No live model inference"],
raw_model_output=partial_raw,
metadata={"adapter": "LLMConnectAdapter", "degraded": True},
)

18
src/cya/llm/factory.py Normal file
View File

@@ -0,0 +1,18 @@
"""Adapter selection factory (CYA-WP-0008-T04)."""
from __future__ import annotations
from cya.config import LLMSettings, load_llm_settings
from cya.llm.adapter import FakeLLMAdapter, LLMAdapter
from cya.llm.connect_adapter import LLMConnectAdapter
def get_adapter(*, offline: bool = False, settings: LLMSettings | None = None) -> LLMAdapter:
"""Return the active LLMAdapter for one-shot and shell code paths."""
resolved = settings or load_llm_settings(offline=offline)
if resolved.adapter == "connect":
return LLMConnectAdapter(resolved)
return FakeLLMAdapter()
__all__ = ["get_adapter", "load_llm_settings"]

73
src/cya/llm/prompt.py Normal file
View File

@@ -0,0 +1,73 @@
"""Prompt construction for llm-connect delegation (CYA-WP-0008)."""
from __future__ import annotations
import json
from typing import Any
from cya.llm.adapter import AssistanceRequest
_DEFAULT_SYSTEM = """You are cya, a console-native assistant for practical local work from the shell.
Help the user with command-line tasks: repository inspection, file workflows, command
suggestion, command explanation, and local context summarization.
Be concise and practical. When suggesting shell commands, explain risks briefly.
Do not claim to have executed anything — the user runs commands themselves.
Reference the provided context when it is relevant."""
def default_system_prompt() -> str:
return _DEFAULT_SYSTEM
def build_assistance_prompt(request: AssistanceRequest, *, system_prompt: str | None = None) -> tuple[str, str]:
"""Return (system_prompt, user_prompt) for llm-connect execute_prompt."""
system = system_prompt or default_system_prompt()
parts: list[str] = []
context = request.context or {}
session_turns = context.get("session_turns")
if session_turns:
parts.append("## Recent conversation")
for turn in session_turns:
parts.append(f"User: {turn.get('user', '')}")
parts.append(f"Assistant: {turn.get('assistant', '')}")
envelope = {k: v for k, v in context.items() if k not in ("session_turns", "memory")}
if envelope:
parts.append("## Local context")
parts.append(_summarize_context(envelope))
memory = context.get("memory")
if isinstance(memory, dict) and memory.get("items"):
parts.append("## Activated memory")
for item in memory["items"][:8]:
parts.append(f"- [{item.get('kind', '?')}] {item.get('key', '?')}: {item.get('value', '')}")
parts.append("## Current request")
parts.append(request.user_request.strip())
return system, "\n\n".join(parts)
def _summarize_context(envelope: dict[str, Any]) -> str:
"""Compact, JSON-safe context summary to stay within prompt budget."""
summary: dict[str, Any] = {}
if envelope.get("cwd"):
summary["cwd"] = envelope["cwd"]
if envelope.get("git"):
git = envelope["git"]
summary["git"] = {
k: git[k]
for k in ("branch", "status_short", "workdir", "is_repo")
if k in git
}
if envelope.get("top_level"):
names = [e.get("name") for e in envelope["top_level"][:30] if e.get("name")]
summary["top_level"] = names
if envelope.get("env"):
summary["env"] = envelope["env"]
if envelope.get("notes"):
summary["notes"] = envelope["notes"][:5]
return json.dumps(summary, indent=2, default=str)

View File

@@ -1,18 +1,29 @@
"""phase-memory ports (T05 → T02) — cya seam to phase-memory (markitect).
"""phase-memory ports — cya seam to phase-memory (markitect).
See CYA-WP-0002 T01 contract in MemoryVision.md for full details.
This module is the explicit, inspectable boundary. All memory for
assistance (preferences, project context, etc.) flows through here.
assistance (preferences, project context, retrospection outcomes, etc.)
flows through here.
Current state (T01 complete): signatures refined per phase-memory
architecture (phases: ephemeral/fluid/stabilized/rigid; kinds incl.
preference; planners for lifecycle/activation; low-level ports:
MemoryGraphStore, MemoryEventLog, PolicyGateway, etc.). Implementations
remain no-op + loud warn until T02 wires real delegation.
**Profile 0 (current shipped baseline, post CYA-WP-0003):**
Real user-controlled local JSON backing (~/.config/cya/memory/<scope>.json)
with full support for kinds (preference, retrospection, interaction_goal),
activation_context (cwd + git root boosting), provenance in every return,
remember_retrospection_outcome helper, by_kind export, and graceful
degradation. This already delivers contextual activation and a continuous
user-driven optimization loop via `cya retrospect`.
Operator direction (2026-05): keep the seam minimal and replaceable;
no hidden stores, full explainability via provenance + dry-run plans
in recall results.
See:
- MemoryVision.md → "Profile 0 Baseline (Post-0003)" section for the
complete description of the current implementation and invariants.
- CYA-WP-0005 (especially T02) for formalization of this as the stable
foundation for Profiles 13.
- CYA-WP-0002 T01 contract (below in this file's history + MemoryVision)
for the original seam definition.
The public API signatures are stable. Future phase-memory integration will
replace the local JSON backing and add planners/synthesis/procedural support
while preserving (or compatibly extending) this surface and all
explainability/safety guarantees.
"""
from __future__ import annotations
@@ -27,6 +38,8 @@ from typing import Any
KIND_PREFERENCE = "preference"
KIND_RETROSPECTION = "retrospection"
KIND_INTERACTION_GOAL = "interaction_goal"
KIND_REFLECTION = "reflection" # Profile 1 (Reflexion-style verbal lessons) — T05 minimal spike
KIND_SESSION_TURN = "session_turn" # CYA-WP-0007 episodic shell turn precursor
def _warn_not_connected(feature: str) -> None:
@@ -87,6 +100,7 @@ def remember_preference(
profile: str | None = None,
ttl: str | None = None,
kind: str = KIND_PREFERENCE,
provenance: dict[str, Any] | None = None,
) -> None:
"""Remember a user preference, workflow pattern, retrospection outcome, or goal.
@@ -106,6 +120,8 @@ def remember_preference(
"profile": profile,
"kind": kind,
}
if provenance:
item["provenance"] = provenance
# avoid exact dups for same key in small stores
items = [i for i in items if i.get("key") != key]
items.append(item)
@@ -154,7 +170,13 @@ def recall_preferences(
normal.append(item)
items = boosted + normal
items = items[-limit:] # most recent first (after boosting)
# Profile 1: prefer reflections ahead of other kinds when requested
if kinds and KIND_REFLECTION in kinds:
reflections = [i for i in items if i.get("kind") == KIND_REFLECTION]
others = [i for i in items if i.get("kind") != KIND_REFLECTION]
items = (reflections + others)[:limit]
else:
items = items[-limit:] # most recent (after boosting)
return {
"items": items,
@@ -210,7 +232,13 @@ def export_memory(scope: str = "cwd", *, profile: str | None = None, kinds: list
k = item.get("kind", "unknown")
by_kind.setdefault(k, []).append(item)
return {
reflection_by_scope: dict[str, int] = {}
for item in items:
if item.get("kind") == KIND_REFLECTION:
s = str(item.get("scope", scope))
reflection_by_scope[s] = reflection_by_scope.get(s, 0) + 1
result = {
"status": "real (T02+0003 local json; activation + retrospection ready)",
"scope": scope,
"profile": profile,
@@ -222,6 +250,10 @@ def export_memory(scope: str = "cwd", *, profile: str | None = None, kinds: list
"note": "User-controlled. Replace with real phase-memory when available.",
"phases": ["ephemeral", "fluid", "stabilized", "rigid"],
}
if reflection_by_scope:
result["reflection_counts_by_scope"] = reflection_by_scope
result["reflection_count"] = sum(reflection_by_scope.values())
return result
except Exception as e:
_warn_not_connected(f"export_memory(scope={scope}, profile={profile}) err={e}")
return {
@@ -246,14 +278,40 @@ def remember_retrospection_outcome(
remember_preference(key, value, scope=scope, profile=profile, kind=kind)
def remember_reflection(
key: str,
value: Any,
scope: str = "cwd",
*,
profile: str | None = None,
provenance: dict[str, Any] | None = None,
) -> None:
"""Convenience helper for Profile 1 (Reflexion-style verbal self-improvement).
Stores verbal lessons/reflections with kind="reflection" for preferential
activation in future turns.
"""
remember_preference(
key,
value,
scope=scope,
profile=profile,
kind=KIND_REFLECTION,
provenance=provenance,
)
__all__ = [
"remember_preference",
"recall_preferences",
"forget",
"export_memory",
"remember_retrospection_outcome",
"remember_reflection",
"KIND_PREFERENCE",
"KIND_RETROSPECTION",
"KIND_INTERACTION_GOAL",
"KIND_REFLECTION",
"KIND_SESSION_TURN",
]

View File

@@ -0,0 +1,241 @@
"""Profile 1 reflection helpers — capture, compaction, and surfacing.
Pure, testable logic for verbal reflection management. Interactive CLI flows
in orchestrator.py call into these functions.
"""
from __future__ import annotations
import re
from datetime import datetime, timezone
from difflib import SequenceMatcher
from typing import Any
from cya.memory import (
KIND_REFLECTION,
_load,
_save,
export_memory,
remember_reflection,
)
REFLECTION_CAPTURE_PROMPTS: tuple[tuple[str, str], ...] = (
("went_well", "What went well that you want to remember? (or 'skip')"),
("remember", "What should cya remember for next time? (or 'skip')"),
("avoid", "What should cya avoid in this scope? (or 'skip')"),
)
_SKIP_VALUES = frozenset({"", "skip", "s", "n", "no"})
_MAX_SURFACING_LESSONS = 3
_MAX_LESSON_CHARS_EXPLAIN = 80
_MAX_LESSON_CHARS_RESPONSE = 60
_SIMILARITY_THRESHOLD = 0.85
def is_skip_answer(text: str) -> bool:
return not text or text.strip().lower() in _SKIP_VALUES
def collect_lessons_from_answers(answers: dict[str, str]) -> list[dict[str, str]]:
"""Build lesson records from guided prompt answers; skips empty/skip answers."""
lessons: list[dict[str, str]] = []
for prompt_key, _label in REFLECTION_CAPTURE_PROMPTS:
raw = answers.get(prompt_key, "")
if is_skip_answer(raw):
continue
text = raw.strip()
if text:
lessons.append({"prompt": prompt_key, "text": text})
return lessons
def preview_lessons(lessons: list[dict[str, str]]) -> str:
if not lessons:
return "(no lessons to save)"
lines = []
for i, lesson in enumerate(lessons, 1):
label = lesson.get("prompt", "lesson").replace("_", " ")
lines.append(f"{i}. [{label}] {lesson['text']}")
return "\n".join(lines)
def session_provenance(scope: str) -> dict[str, Any]:
return {
"session_date": datetime.now(timezone.utc).strftime("%Y-%m-%d"),
"scope": scope,
"source": "cya retrospect",
}
def save_reflection_lessons(
lessons: list[dict[str, str]],
scope: str,
*,
provenance: dict[str, Any] | None = None,
) -> int:
"""Persist confirmed lessons; returns count saved. No-op for empty input."""
if not lessons:
return 0
meta = provenance or session_provenance(scope)
saved = 0
for lesson in lessons:
key = f"reflection_{lesson['prompt']}_{meta['session_date']}"
remember_reflection(
key,
lesson["text"],
scope=scope,
provenance={**meta, "prompt": lesson["prompt"]},
)
saved += 1
return saved
def normalize_reflection_text(text: str) -> str:
collapsed = re.sub(r"\s+", " ", text.strip().lower())
return re.sub(r"[^\w\s]", "", collapsed)
def reflection_similarity(a: str, b: str) -> float:
na, nb = normalize_reflection_text(a), normalize_reflection_text(b)
if not na or not nb:
return 0.0
if na == nb:
return 1.0
return SequenceMatcher(None, na, nb).ratio()
def find_duplicate_reflection_groups(
scope: str,
*,
threshold: float = _SIMILARITY_THRESHOLD,
) -> list[list[dict[str, Any]]]:
"""Return groups of near-duplicate reflection items in the same scope."""
items = [i for i in _load(scope) if i.get("kind") == KIND_REFLECTION]
groups: list[list[dict[str, Any]]] = []
used: set[str] = set()
for i, item in enumerate(items):
key_i = item.get("key", str(i))
if key_i in used:
continue
group = [item]
val_i = str(item.get("value", ""))
for j, other in enumerate(items):
if j <= i:
continue
key_j = other.get("key", str(j))
if key_j in used:
continue
val_j = str(other.get("value", ""))
if reflection_similarity(val_i, val_j) >= threshold:
group.append(other)
used.add(key_j)
if len(group) > 1:
groups.append(group)
used.add(key_i)
return groups
def compact_reflections(
scope: str,
*,
keep_key: str,
remove_keys: list[str],
merged_value: str | None = None,
) -> dict[str, Any]:
"""Explicit opt-in compaction: update keeper and remove duplicates."""
items = _load(scope)
removed: list[str] = []
updated = False
for item in items:
if item.get("key") == keep_key and item.get("kind") == KIND_REFLECTION:
if merged_value is not None:
item["value"] = merged_value
updated = True
new_items = []
for item in items:
k = item.get("key")
if k in remove_keys and item.get("kind") == KIND_REFLECTION:
removed.append(k)
continue
new_items.append(item)
if removed or updated:
_save(scope, new_items)
return {
"kept": keep_key,
"removed": removed,
"updated": updated,
"remaining_reflections": sum(
1 for i in new_items if i.get("kind") == KIND_REFLECTION
),
}
def list_reflections(scope: str) -> list[dict[str, Any]]:
exported = export_memory(scope, kinds=[KIND_REFLECTION])
return exported.get("items", [])
def format_reflection_surfacing(
memory: dict[str, Any],
*,
for_explain: bool = False,
) -> str | None:
"""Budget-capped reflection summary for explain-context or normal responses."""
items = memory.get("items", []) if isinstance(memory, dict) else []
reflections = [i for i in items if i.get("kind") == KIND_REFLECTION]
if not reflections:
return None
max_chars = _MAX_LESSON_CHARS_EXPLAIN if for_explain else _MAX_LESSON_CHARS_RESPONSE
snippets: list[str] = []
for item in reflections[:_MAX_SURFACING_LESSONS]:
text = str(item.get("value", "")).strip()
if len(text) > max_chars:
text = text[: max_chars - 3] + "..."
prov = item.get("provenance") or {}
date = prov.get("session_date", "")
prefix = f"({date}) " if date and for_explain else ""
snippets.append(f"{prefix}{text}")
extra = len(reflections) - _MAX_SURFACING_LESSONS
suffix = f" (+{extra} more)" if extra > 0 else ""
count = len(reflections)
noun = "reflection" if count == 1 else "reflections"
return f"{count} verbal {noun} influenced this{suffix}: " + "; ".join(snippets)
def reflection_export_stats(scope: str) -> dict[str, Any]:
"""Observability: reflection counts and scope breakdown for export."""
exported = export_memory(scope, kinds=[KIND_REFLECTION])
items = exported.get("items", [])
by_scope: dict[str, int] = {}
for item in items:
s = item.get("scope", scope)
by_scope[s] = by_scope.get(s, 0) + 1
return {
"reflection_count": len(items),
"reflection_counts_by_scope": by_scope,
"scope": scope,
}
__all__ = [
"REFLECTION_CAPTURE_PROMPTS",
"collect_lessons_from_answers",
"preview_lessons",
"session_provenance",
"save_reflection_lessons",
"normalize_reflection_text",
"reflection_similarity",
"find_duplicate_reflection_groups",
"compact_reflections",
"list_reflections",
"format_reflection_surfacing",
"reflection_export_stats",
"is_skip_answer",
]

View File

@@ -1,26 +1,33 @@
"""Assistance orchestrator (T06).
"""Assistance orchestrator.
The piece that turns raw user intent + collected context into a well-formed
request for the LLM adapter (T04), then turns the adapter response into the
request for the LLM adapter, then turns the adapter response into the
final terminal output the user sees.
Responsibilities in this slice:
- Own the end-to-end happy path after Typer argument parsing.
- Coordinate context collector (T02), risk classifier (T03), and LLMAdapter (T04).
- Keep the CLI surface (main.py) thin — it should only do argument parsing,
help/version, and delegation to this orchestrator.
- Be testable in isolation with the FakeLLMAdapter (critical for T07).
Key responsibilities:
- Coordinate context collector, memory (Profile 0 via recall with activation_context),
rule-based risk classifier, and LLMAdapter.
- Wire memory influence into every assistance cycle and `--explain-context`.
- Own the `cya retrospect` flow that feeds higher-order memory back into the system.
- Keep the CLI surface thin.
This module is the natural home for future prompt framing, context packing
with token awareness, safety charter injection, and response post-processing.
**Memory note (Profile 0):** This module is the primary consumer of the
stable memory ports defined in src/cya/memory/__init__.py. All calls use
activation_context derived from the current working directory + git root.
See MemoryVision.md "Profile 0 Baseline" and CYA-WP-0005 for the evolution
path to Profiles 13.
See workplan CYA-WP-0001-T06.
See workplan CYA-WP-0001 (core) + CYA-WP-0003 (memory wiring + retrospect)
+ CYA-WP-0005 (profile formalization).
"""
from __future__ import annotations
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
import typer
from rich.console import Console
from rich.panel import Panel
@@ -30,20 +37,71 @@ from cya.memory import (
remember_retrospection_outcome,
KIND_RETROSPECTION,
KIND_INTERACTION_GOAL,
KIND_REFLECTION,
)
from cya.memory.reflections import (
REFLECTION_CAPTURE_PROMPTS,
collect_lessons_from_answers,
compact_reflections,
find_duplicate_reflection_groups,
format_reflection_surfacing,
preview_lessons,
save_reflection_lessons,
session_provenance,
)
from cya.safety.risk import classify, get_user_confirmation
from cya.llm.adapter import AssistanceRequest, FakeLLMAdapter
from cya.config import bound_session_turns
from cya.llm.adapter import AssistanceRequest
from cya.llm.factory import get_adapter
console = Console()
@dataclass
class OrchestratorResult:
"""Structured result from a single assistance turn.
CLI callers still receive the rich terminal rendering. Interactive clients
use this object to persist session turns and explain the last turn.
"""
user_request: str
assistant: str
explanation: str = ""
rationale: str = ""
context: dict[str, Any] = field(default_factory=dict)
memory: dict[str, Any] = field(default_factory=dict)
risk: dict[str, Any] = field(default_factory=dict)
cancelled: bool = False
dry_run: bool = False
def _build_assistance_context(
envelope: Any,
memory: dict[str, Any],
*,
session_turns: list[dict[str, str]] | None = None,
extra_context: dict[str, Any] | None = None,
) -> dict[str, Any]:
ctx = (envelope.to_dict() if envelope else {}) or {}
ctx["memory"] = memory
if session_turns:
ctx["session_turns"] = bound_session_turns(session_turns)
if extra_context:
ctx["session"] = extra_context
return ctx
def handle_request(
user_request: str,
*,
explain_context: bool = False,
dry_run: bool = False,
) -> None:
offline: bool = False,
session_turns: list[dict[str, str]] | None = None,
extra_context: dict[str, Any] | None = None,
) -> OrchestratorResult:
"""Primary orchestrator entry point.
This is what the CLI (and future tests / other front-ends) should call.
@@ -81,22 +139,32 @@ def handle_request(
git_info = envelope.git or {}
if git_info.get("workdir"):
act_ctx["git_root"] = git_info["workdir"]
memory = recall_preferences(".", activation_context=act_ctx)
memory = recall_preferences(
".",
activation_context=act_ctx,
kinds=[KIND_REFLECTION, KIND_RETROSPECTION, KIND_INTERACTION_GOAL, "preference"],
)
except Exception:
memory = {"error": "recall failed (graceful degradation)"}
if explain_context and memory.get("items"):
try:
prov = memory.get("provenance", [{}])[0]
# Show a couple of activated items for transparency (T03 0003)
sample = ", ".join(i.get("key", "?") for i in memory.get("items", [])[:3])
act_note = ""
if prov.get("activation_context"):
act_note = f" | ctx: {prov['activation_context']}"
body = (
f"Phase: {memory.get('phase')} | {len(memory.get('items', []))} items | "
f"{prov.get('source', 'local')}{act_note}\n"
f"Sample activated: {sample}"
)
refl_line = format_reflection_surfacing(memory, for_explain=True)
if refl_line:
body += f"\n\n[cyan]Reflections:[/cyan] {refl_line}"
console.print(
Panel(
f"Phase: {memory.get('phase')} | {len(memory.get('items', []))} items | {prov.get('source', 'local')}{act_note}\n"
f"Sample activated: {sample}",
body,
title="Memory Activated (T03)",
border_style="blue",
padding=(0, 1),
@@ -105,8 +173,29 @@ def handle_request(
except Exception:
pass
if explain_context and extra_context:
try:
import json
console.print(
Panel(
json.dumps(extra_context, indent=2, default=str),
title="Shell Session Context",
border_style="cyan",
padding=(0, 1),
)
)
except Exception:
pass
# 2. Risk classification + mandatory confirmation (T03 safety; T04 memory signals)
assessment = classify(user_request, envelope, memory=memory)
ctx = _build_assistance_context(
envelope,
memory,
session_turns=session_turns,
extra_context=extra_context,
)
if assessment.requires_confirmation:
from rich.table import Table
@@ -125,17 +214,31 @@ def handle_request(
console.print(table)
if not get_user_confirmation(assessment):
console.print("[yellow]Action cancelled by user. No changes made.[/yellow]")
return
msg = "Action cancelled by user. No changes made."
console.print(f"[yellow]{msg}[/yellow]")
return OrchestratorResult(
user_request=user_request,
assistant=msg,
context=ctx,
memory=memory,
risk=assessment.to_dict(),
cancelled=True,
)
if dry_run:
console.print("[green]--dry-run acknowledged.[/green] No side-effects.")
return
msg = "--dry-run acknowledged. No side-effects."
console.print(f"[green]{msg}[/green]")
return OrchestratorResult(
user_request=user_request,
assistant=msg,
context=ctx,
memory=memory,
risk=assessment.to_dict(),
dry_run=True,
)
# 3. Call through the single LLMAdapter boundary (T04)
adapter = FakeLLMAdapter()
ctx = (envelope.to_dict() if envelope else {}) or {}
ctx["memory"] = memory # T03: memory now in context passed to LLM (for personalization + explain)
# 3. Call through the single LLMAdapter boundary (T04 / CYA-WP-0008)
adapter = get_adapter(offline=offline)
llm_request = AssistanceRequest(
user_request=user_request,
context=ctx,
@@ -146,6 +249,10 @@ def handle_request(
mem_line = ""
if memory.get("items"):
mem_line = f"\n[dim]Memory activated: {len(memory.get('items', []))} items (phase {memory.get('phase')})[/dim]"
refl_line = format_reflection_surfacing(memory, for_explain=False)
if refl_line:
mem_line += f"\n[cyan]{refl_line}[/cyan]"
console.print(
Panel(
f"[bold]Suggestion:[/bold]\n{llm_response.suggestion}\n\n"
@@ -161,6 +268,16 @@ def handle_request(
"[green]✓[/green] Request processed by orchestrator (T02+T03+T04 coordinated by T06)."
)
return OrchestratorResult(
user_request=user_request,
assistant=llm_response.suggestion,
explanation=llm_response.explanation,
rationale=llm_response.rationale,
context=ctx,
memory=memory,
risk=assessment.to_dict(),
)
def run_retrospection(scope: str = ".", limit: int = 8) -> None:
"""Guided retrospection session (T04 of CYA-WP-0003).
@@ -247,11 +364,15 @@ def run_retrospection(scope: str = ".", limit: int = 8) -> None:
)
console.print("[green]Recorded as safety preference.[/green]")
_capture_reflection_lessons(scope)
_offer_reflection_compaction(scope)
console.print(
Panel(
"Thank you. Your reflections have been stored as retrospection memory.\n"
"They will be preferentially activated in future sessions in this scope.\n\n"
"You can review them anytime with:\n"
f" [bold]cya memory reflections --scope {scope}[/bold]\n"
f" [bold]cya --explain-context \"...\"[/bold] (in this directory)\n"
f" or inspect the JSON files in [cyan]~/.config/cya/memory/[/cyan]",
title="Retrospection Complete",
@@ -261,4 +382,91 @@ def run_retrospection(scope: str = ".", limit: int = 8) -> None:
)
__all__ = ["handle_request", "run_retrospection"]
def _capture_reflection_lessons(scope: str) -> None:
"""Profile 1: guided verbal lesson capture with preview and confirmation."""
want = typer.prompt(
"Capture 13 verbal lessons from this session? (y/n)",
default="n",
show_default=False,
)
if not want or want.strip().lower() in ("n", "no", "skip", "s", ""):
return
answers: dict[str, str] = {}
for prompt_key, label in REFLECTION_CAPTURE_PROMPTS:
answers[prompt_key] = typer.prompt(label, default="", show_default=False)
lessons = collect_lessons_from_answers(answers)
if not lessons:
console.print("[yellow]No lessons captured — nothing stored.[/yellow]")
return
console.print(
Panel(
preview_lessons(lessons),
title="Preview — verbal lessons to save",
border_style="cyan",
)
)
confirm = typer.prompt("Save these lessons? (y/n)", default="n", show_default=False)
if confirm.strip().lower() not in ("y", "yes"):
console.print("[yellow]Lessons discarded — nothing stored.[/yellow]")
return
count = save_reflection_lessons(
lessons,
scope,
provenance=session_provenance(scope),
)
console.print(f"[green]Saved {count} verbal reflection(s) (Profile 1).[/green]")
def _offer_reflection_compaction(scope: str) -> None:
"""Offer explicit merge of near-duplicate reflections in this scope."""
groups = find_duplicate_reflection_groups(scope)
if not groups:
return
console.print(
Panel(
f"Found {len(groups)} group(s) of similar reflections in scope [green]{scope}[/green].\n"
"Compaction is opt-in — nothing is deleted without your confirmation.",
title="Reflection compaction available",
border_style="yellow",
)
)
do_compact = typer.prompt("Review and compact duplicates? (y/n)", default="n", show_default=False)
if do_compact.strip().lower() not in ("y", "yes"):
return
for group in groups:
console.print("\n[bold]Similar reflections:[/bold]")
for item in group:
console.print(f"{item.get('key')}: {item.get('value')}")
keep = typer.prompt(
"Key to keep (or 'skip' this group)",
default=group[0].get("key", ""),
show_default=True,
)
if not keep or keep.strip().lower() in ("skip", "s"):
continue
remove_keys = [i.get("key") for i in group if i.get("key") != keep]
merge = typer.prompt(
"Merged text (Enter to keep existing value of kept key)",
default="",
show_default=False,
)
result = compact_reflections(
scope,
keep_key=keep,
remove_keys=[k for k in remove_keys if k],
merged_value=merge.strip() or None,
)
console.print(
f"[green]Compacted: kept {result['kept']}, removed {len(result['removed'])}[/green]"
)
__all__ = ["OrchestratorResult", "handle_request", "run_retrospection"]

862
src/cya/shell_session.py Normal file
View File

@@ -0,0 +1,862 @@
"""Interactive shell session runtime for ``cya shell`` (CYA-WP-0007).
The shell is a thin interactive client for the existing orchestrator. It owns
session-local state, optional history context, State Hub orientation, slash
commands, JSONL persistence, and user-triggered learning/export flows.
"""
from __future__ import annotations
import json
import os
import re
import shlex
import sys
import uuid
from dataclasses import asdict, dataclass, field
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Callable
from urllib import parse, request
import typer
from rich.console import Console
from rich.panel import Panel
from rich.table import Table
from cya.config import ShellHistorySettings, load_shell_history_settings
from cya.memory import KIND_SESSION_TURN, remember_preference
from cya.memory.reflections import (
REFLECTION_CAPTURE_PROMPTS,
collect_lessons_from_answers,
preview_lessons,
save_reflection_lessons,
)
from cya.orchestrator import OrchestratorResult, handle_request
TOPIC_ID = "64418556-3206-457a-ba29-6884b5b12cf3"
DEFAULT_AGENT = "can-you-assist"
DEFAULT_HUB_URL = os.environ.get("CYA_STATE_HUB_URL", "http://127.0.0.1:8000")
@dataclass
class ShellHistoryContext:
enabled: bool
source: str
limit: int
histfile: str | None = None
lines: list[dict[str, Any]] = field(default_factory=list)
redactions: int = 0
notes: list[str] = field(default_factory=list)
def to_dict(self) -> dict[str, Any]:
return asdict(self)
@dataclass
class HubOrientation:
base_url: str
topic_id: str
agent: str
brief: str | None = None
active_workstreams: list[dict[str, Any]] = field(default_factory=list)
inbox: list[dict[str, Any]] = field(default_factory=list)
errors: list[str] = field(default_factory=list)
remote_checked: bool = False
@property
def hub_available(self) -> bool:
return self.remote_checked and not self.errors
def to_dict(self) -> dict[str, Any]:
data = asdict(self)
data["hub_available"] = self.hub_available
return data
def compact(self) -> dict[str, Any]:
return {
"base_url": self.base_url,
"topic_id": self.topic_id,
"agent": self.agent,
"hub_available": self.hub_available,
"active_workstream_count": len(self.active_workstreams),
"inbox_count": len(self.inbox),
"errors": self.errors,
"workstreams": [
{
"id": w.get("id") or w.get("workstream_id"),
"title": w.get("title") or w.get("name"),
"status": w.get("status"),
}
for w in self.active_workstreams[:5]
],
}
_SECRET_PATTERNS: tuple[re.Pattern[str], ...] = (
re.compile(
r"(?i)\b([A-Z0-9_]*(?:API[_-]?KEY|TOKEN|PASSWORD|PASSWD|SECRET)[A-Z0-9_]*)"
r"(\s*=\s*)([^\s;]+)"
),
re.compile(r"(?i)\b(--(?:api-key|token|password|secret)\s+)([^\s;]+)"),
re.compile(r"\b(sk-[A-Za-z0-9_-]{8,})\b"),
re.compile(r"\b(gh[pousr]_[A-Za-z0-9_]{12,})\b"),
re.compile(r"\b(xox[baprs]-[A-Za-z0-9-]{12,})\b"),
re.compile(r"\b(AKIA[0-9A-Z]{12,})\b"),
)
InputFunc = Callable[[str], str]
def utc_now() -> str:
return datetime.now(timezone.utc).isoformat()
def make_session_id() -> str:
stamp = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S")
return f"cya-{stamp}-{uuid.uuid4().hex[:8]}"
def session_root() -> Path:
root = Path.home() / ".config" / "cya" / "sessions"
root.mkdir(parents=True, exist_ok=True)
return root
def default_session_path(session_id: str) -> Path:
return session_root() / f"{session_id}.jsonl"
def redact_secrets(text: str) -> tuple[str, int]:
redacted = text
count = 0
def _replace(match: re.Match[str]) -> str:
groups = match.groups()
if len(groups) >= 3:
return f"{groups[0]}{groups[1]}[REDACTED]"
return "[REDACTED_SECRET]"
for pattern in _SECRET_PATTERNS:
redacted, n = pattern.subn(_replace, redacted)
count += n
return redacted, count
def _normalize_history_line(line: str) -> str:
text = line.strip()
if text.startswith(": ") and ";" in text:
return text.split(";", 1)[1].strip()
if text.startswith("- cmd: "):
return text.removeprefix("- cmd: ").strip()
return text
def _default_histfile(env: dict[str, str], home: Path) -> Path | None:
explicit = env.get("HISTFILE")
if explicit:
return Path(explicit).expanduser()
for candidate in (
home / ".bash_history",
home / ".zsh_history",
home / ".local" / "share" / "fish" / "fish_history",
):
if candidate.is_file():
return candidate
return None
def collect_shell_history(
settings: ShellHistorySettings,
*,
env: dict[str, str] | None = None,
home: Path | None = None,
) -> ShellHistoryContext:
env = env or os.environ
home = home or Path.home()
ctx = ShellHistoryContext(
enabled=settings.enabled,
source=settings.source,
limit=settings.limit,
histfile=settings.histfile,
notes=list(settings.notes),
)
if not settings.enabled:
ctx.notes.append("Shell history disabled; no history was collected.")
return ctx
if settings.limit <= 0:
ctx.notes.append("Shell history enabled with limit 0; no lines collected.")
return ctx
histfile = Path(settings.histfile).expanduser() if settings.histfile else _default_histfile(env, home)
if not histfile:
ctx.notes.append("No shell history file found from HISTFILE or known fallbacks.")
return ctx
ctx.histfile = str(histfile)
try:
raw = histfile.read_text(encoding="utf-8", errors="replace").splitlines()
except OSError as exc:
ctx.notes.append(f"Could not read history file: {exc}")
return ctx
normalized = [_normalize_history_line(line) for line in raw]
normalized = [line for line in normalized if line]
start = max(0, len(normalized) - settings.limit)
for offset, line in enumerate(normalized[start:], start=start + 1):
clean, count = redact_secrets(line[:1000])
ctx.redactions += count
ctx.lines.append(
{
"line_number": offset,
"command": clean,
"source": str(histfile),
"provenance": "shell_history.histfile",
}
)
if ctx.redactions:
ctx.notes.append(f"Redacted {ctx.redactions} secret-like value(s).")
return ctx
def render_shell_history_explanation(history: ShellHistoryContext) -> str:
lines = [
f"enabled: {history.enabled}",
f"source: {history.source}",
f"limit: {history.limit}",
f"histfile: {history.histfile or '(none)'}",
f"lines: {len(history.lines)}",
]
if history.notes:
lines.append("notes:")
lines.extend(f" - {note}" for note in history.notes)
if history.lines:
lines.append("commands:")
for item in history.lines:
lines.append(
f" {item['line_number']}: {item['command']} ({item['provenance']})"
)
return "\n".join(lines)
def _read_json_url(url: str, *, timeout: float) -> Any:
req = request.Request(url, headers={"User-Agent": "cya-shell/0.1"})
with request.urlopen(req, timeout=timeout) as resp:
data = resp.read().decode("utf-8")
if not data.strip():
return []
return json.loads(data)
def _post_json_url(url: str, payload: dict[str, Any], *, timeout: float = 3.0) -> Any:
body = json.dumps(payload).encode("utf-8")
req = request.Request(
url,
data=body,
method="POST",
headers={"Content-Type": "application/json", "User-Agent": "cya-shell/0.1"},
)
with request.urlopen(req, timeout=timeout) as resp:
data = resp.read().decode("utf-8")
if not data.strip():
return {}
return json.loads(data)
def _patch_json_url(url: str, payload: dict[str, Any], *, timeout: float = 3.0) -> Any:
body = json.dumps(payload).encode("utf-8")
req = request.Request(
url,
data=body,
method="PATCH",
headers={"Content-Type": "application/json", "User-Agent": "cya-shell/0.1"},
)
with request.urlopen(req, timeout=timeout) as resp:
data = resp.read().decode("utf-8")
if not data.strip():
return {}
return json.loads(data)
def _as_list(value: Any) -> list[dict[str, Any]]:
if isinstance(value, list):
return [item for item in value if isinstance(item, dict)]
if isinstance(value, dict):
for key in ("items", "results", "data"):
if isinstance(value.get(key), list):
return [item for item in value[key] if isinstance(item, dict)]
return []
def load_hub_orientation(
*,
cwd: Path | None = None,
base_url: str = DEFAULT_HUB_URL,
topic_id: str = TOPIC_ID,
agent: str = DEFAULT_AGENT,
timeout: float = 1.5,
include_remote: bool = True,
) -> HubOrientation:
cwd = cwd or Path.cwd()
orientation = HubOrientation(base_url=base_url.rstrip("/"), topic_id=topic_id, agent=agent)
brief = cwd / ".custodian-brief.md"
if brief.is_file():
try:
orientation.brief = brief.read_text(encoding="utf-8", errors="replace")[:6000]
except OSError as exc:
orientation.errors.append(f"brief read failed: {exc}")
if not include_remote:
orientation.errors.append("State Hub remote orientation skipped by --no-hub.")
return orientation
try:
qs = parse.urlencode({"topic_id": topic_id, "status": "active"})
data = _read_json_url(f"{orientation.base_url}/workstreams/?{qs}", timeout=timeout)
orientation.active_workstreams = _as_list(data)
orientation.remote_checked = True
except Exception as exc:
orientation.errors.append(f"workstreams unavailable: {exc}")
try:
qs = parse.urlencode({"to_agent": agent, "unread_only": "true"})
data = _read_json_url(f"{orientation.base_url}/messages/?{qs}", timeout=timeout)
orientation.inbox = _as_list(data)
orientation.remote_checked = True
except Exception as exc:
orientation.errors.append(f"inbox unavailable: {exc}")
return orientation
def render_hub_summary(orientation: HubOrientation) -> str:
lines: list[str] = []
if orientation.brief:
for line in orientation.brief.splitlines():
clean = line.strip("# ").strip()
if clean:
lines.append(clean)
break
if orientation.remote_checked:
lines.append(f"Active workstreams: {len(orientation.active_workstreams)}")
lines.append(f"Unread inbox messages: {len(orientation.inbox)}")
else:
lines.append("State Hub remote orientation not available.")
if orientation.errors:
lines.append("Notes: " + "; ".join(orientation.errors[:2]))
return "\n".join(lines)
def render_hub_details(orientation: HubOrientation) -> str:
lines = [render_hub_summary(orientation), ""]
if orientation.active_workstreams:
lines.append("Workstreams:")
for ws in orientation.active_workstreams[:8]:
title = ws.get("title") or ws.get("name") or "(untitled)"
ident = ws.get("id") or ws.get("workstream_id") or "?"
status = ws.get("status") or "?"
lines.append(f" - {title} [{status}] {ident}")
tasks = ws.get("tasks") or ws.get("open_tasks") or []
if isinstance(tasks, list):
for task in tasks[:5]:
if isinstance(task, dict):
t_title = task.get("title") or task.get("name") or "(task)"
t_status = task.get("status") or "?"
lines.append(f" * {t_title} [{t_status}]")
if orientation.inbox:
lines.append("\nInbox:")
for msg in orientation.inbox[:10]:
title = msg.get("title") or msg.get("summary") or msg.get("subject") or "(message)"
ident = msg.get("id") or "?"
lines.append(f" - {ident}: {title}")
if orientation.errors:
lines.append("\nErrors:")
lines.extend(f" - {err}" for err in orientation.errors)
return "\n".join(lines).strip()
def post_progress(summary: str, orientation: HubOrientation) -> dict[str, Any]:
return _post_json_url(
f"{orientation.base_url}/progress/",
{"summary": summary, "event_type": "note", "author": "cya-shell"},
)
def mark_message_read(message_id: str, orientation: HubOrientation) -> dict[str, Any]:
return _patch_json_url(f"{orientation.base_url}/messages/{message_id}/read", {})
def detect_weakness_hints(
user_text: str,
assistant_text: str = "",
*,
risk: dict[str, Any] | None = None,
turn_history: list[dict[str, Any]] | None = None,
hub: HubOrientation | None = None,
) -> list[dict[str, str]]:
text = f"{user_text}\n{assistant_text}".lower()
risk = risk or {}
turn_history = turn_history or []
hints: list[dict[str, str]] = []
secret_terms = ("api key", "token", "password", "secret", "openrouter", "ops_hub_key")
if any(term in text for term in secret_terms) and "warden route" not in text:
hints.append(
{
"rule": "credential-routing",
"title": "Credential routing",
"message": "Route secret custody first with warden route/OpenBao; do not paste keys into prompts, Git, State Hub, or logs.",
}
)
if "ops-warden" in text and any(term in text for term in secret_terms):
hints.append(
{
"rule": "ops-warden-secret-anti-pattern",
"title": "ops-warden boundary",
"message": "ops-warden issues SSH certificates only; API keys and passwords belong to their routed owner.",
}
)
if re.search(r"\b(curl|wget)\b.*\|\s*(bash|sh|zsh|python)", text, re.S):
hints.append(
{
"rule": "remote-exec-review",
"title": "Remote execution review",
"message": "Inspect downloaded content before execution and keep the safety confirmation gate intact.",
}
)
level = str(risk.get("level", "")).lower()
previous_destructive = any(
str(turn.get("risk", "")).lower() in {"destructive", "mass_edit"}
for turn in turn_history[-3:]
)
if level in {"destructive", "mass_edit"} and previous_destructive:
hints.append(
{
"rule": "repeated-destructive-intent",
"title": "Repeated destructive intent",
"message": "Repeated destructive or mass-edit turns are a cue to slow down and verify scope before proceeding.",
}
)
wp_tokens = set(re.findall(r"\b[A-Z]{2,5}-WP-\d{4}\b", user_text.upper()))
if wp_tokens and hub is not None:
haystack = json.dumps(hub.compact(), default=str).upper()
missing = sorted(token for token in wp_tokens if token not in haystack)
if missing:
hints.append(
{
"rule": "state-hub-alignment",
"title": "State Hub alignment",
"message": f"{', '.join(missing)} was not visible in the loaded hub summary; confirm the local workplan file is the source of truth.",
}
)
return hints
def render_weakness_hints(hints: list[dict[str, str]]) -> str:
lines = []
for hint in hints:
lines.append(f"[{hint['rule']}] {hint['message']}")
return "\n".join(lines)
class ShellSession:
def __init__(
self,
*,
session_id: str | None = None,
offline: bool = False,
history: ShellHistoryContext | None = None,
hub: HubOrientation | None = None,
console: Console | None = None,
input_func: InputFunc | None = None,
interactive: bool | None = None,
offer_end_learning: bool = True,
) -> None:
self.session_id = session_id or make_session_id()
self.path = default_session_path(self.session_id)
self.offline = offline
self.history = history or ShellHistoryContext(False, "default", 0)
self.hub = hub or HubOrientation(DEFAULT_HUB_URL, TOPIC_ID, DEFAULT_AGENT)
self.console = console or Console()
self.input_func = input_func or input
self.interactive = sys.stdin.isatty() if interactive is None else interactive
self.offer_end_learning = offer_end_learning
self.turns: list[dict[str, Any]] = []
self.last_result: OrchestratorResult | None = None
self.last_hints: list[dict[str, str]] = []
self._started = False
def append_event(self, event: dict[str, Any]) -> None:
self.path.parent.mkdir(parents=True, exist_ok=True)
with self.path.open("a", encoding="utf-8") as fh:
fh.write(json.dumps(event, default=str) + "\n")
def start(self) -> None:
if self._started:
return
self._started = True
self.append_event(
{
"kind": "session_start",
"session_id": self.session_id,
"created_at": utc_now(),
"cwd": str(Path.cwd()),
"history": self.history.to_dict(),
"hub": self.hub.compact(),
}
)
body = (
f"Session: {self.session_id}\n"
f"Artifact: {self.path}\n\n"
f"{render_hub_summary(self.hub)}\n\n"
f"History lines: {len(self.history.lines)}"
)
self.console.print(Panel(body, title="cya shell", border_style="cyan"))
self.console.print("Type /help for commands, /exit to finish.")
try:
import readline # noqa: F401
except Exception:
pass
def run(self) -> Path:
self.start()
while True:
try:
line = self.input_func("cya> ")
except KeyboardInterrupt:
self.console.print("[yellow]Interrupted. Use /exit to end the session.[/yellow]")
continue
except EOFError:
self.console.print("[dim]EOF received; ending session.[/dim]")
break
text = line.strip()
if not text:
continue
if text.startswith("/"):
keep_going = self.handle_command(text)
if not keep_going:
break
continue
self.handle_turn(text)
self.close()
return self.path
def handle_turn(self, text: str) -> OrchestratorResult:
extra_context = {
"session_id": self.session_id,
"session_path": str(self.path),
"shell_history": self.history.to_dict(),
"hub_orientation": self.hub.compact(),
}
result = handle_request(
text,
offline=self.offline,
session_turns=list(self.turns),
extra_context=extra_context,
)
hints = detect_weakness_hints(
text,
result.assistant,
risk=result.risk,
turn_history=self.turns,
hub=self.hub,
)
if hints:
self.console.print(
Panel(render_weakness_hints(hints), title="Weakness Hints", border_style="yellow")
)
clean_user, red_user = redact_secrets(text)
clean_assistant, red_assistant = redact_secrets(result.assistant)
risk_level = result.risk.get("level", "safe")
turn = {
"user": text,
"assistant": result.assistant,
"risk": risk_level,
}
self.turns.append(turn)
self.last_result = result
self.last_hints = hints
self.append_event(
{
"kind": KIND_SESSION_TURN,
"session_id": self.session_id,
"turn_index": len(self.turns),
"created_at": utc_now(),
"user": clean_user,
"assistant": clean_assistant,
"risk": result.risk,
"cancelled": result.cancelled,
"dry_run": result.dry_run,
"weakness_hints": hints,
"redactions": red_user + red_assistant,
}
)
return result
def handle_command(self, text: str) -> bool:
try:
parts = shlex.split(text)
except ValueError as exc:
self.console.print(f"[red]Could not parse command: {exc}[/red]")
return True
if not parts:
return True
cmd = parts[0].lower()
if cmd in {"/exit", "/quit"}:
return False
if cmd == "/help":
self.show_help()
return True
if cmd == "/explain":
self.show_explain()
return True
if cmd == "/hub":
self.handle_hub(parts)
return True
if cmd == "/inbox":
self.handle_inbox(parts)
return True
if cmd == "/export-session":
self.export_session()
return True
if cmd == "/learn":
self.capture_reflections()
return True
self.console.print(f"[yellow]Unknown command {parts[0]!r}. Try /help.[/yellow]")
return True
def show_help(self) -> None:
self.console.print(
Panel(
"\n".join(
[
"/help - show commands",
"/explain - show context, risk, history, hub, and hints for the last turn",
"/hub - show active State Hub workstreams and tasks",
"/hub log \"summary\" - post a progress note after confirmation",
"/inbox - show unread State Hub messages",
"/inbox read <id> - explicitly mark a message read",
"/export-session - write a redacted session summary JSON",
"/learn - capture Profile 1 reflections now",
"/exit - close the shell session",
]
),
title="cya shell commands",
border_style="cyan",
)
)
def show_explain(self) -> None:
if not self.last_result:
self.console.print("[yellow]No turns yet.[/yellow]")
self.console.print(
Panel(
render_shell_history_explanation(self.history),
title="Shell History Context",
border_style="cyan",
)
)
return
payload = {
"session_id": self.session_id,
"risk": self.last_result.risk,
"context": self.last_result.context,
"weakness_hints": self.last_hints,
}
self.console.print(
Panel(
json.dumps(payload, indent=2, default=str),
title="Last Turn Explanation",
border_style="green",
)
)
def handle_hub(self, parts: list[str]) -> None:
if len(parts) == 1:
self.console.print(Panel(render_hub_details(self.hub), title="State Hub"))
return
if len(parts) >= 3 and parts[1].lower() == "log":
summary = " ".join(parts[2:]).strip()
if not summary:
self.console.print("[yellow]Usage: /hub log \"summary\"[/yellow]")
return
if not self.interactive:
self.console.print("[yellow]Hub writes require an interactive confirmation.[/yellow]")
return
if not typer.confirm(f"Post progress note to {self.hub.base_url}?", default=False):
self.console.print("[yellow]Progress note not posted.[/yellow]")
return
try:
result = post_progress(summary, self.hub)
self.console.print(f"[green]Posted progress note.[/green] {result}")
except Exception as exc:
self.console.print(f"[red]Progress post failed: {exc}[/red]")
return
self.console.print("[yellow]Usage: /hub or /hub log \"summary\"[/yellow]")
def handle_inbox(self, parts: list[str]) -> None:
if len(parts) == 1:
body = "No unread messages." if not self.hub.inbox else render_hub_details(self.hub)
self.console.print(Panel(body, title="State Hub Inbox"))
return
if len(parts) == 3 and parts[1].lower() == "read":
message_id = parts[2]
if not self.interactive:
self.console.print("[yellow]Mark-read requires an interactive session.[/yellow]")
return
try:
result = mark_message_read(message_id, self.hub)
self.console.print(f"[green]Marked message read.[/green] {result}")
except Exception as exc:
self.console.print(f"[red]Mark-read failed: {exc}[/red]")
return
self.console.print("[yellow]Usage: /inbox or /inbox read <id>[/yellow]")
def export_session(self) -> Path:
out = self.path.with_name(f"{self.session_id}-summary.json")
turns = []
for i, turn in enumerate(self.turns, 1):
user, user_redactions = redact_secrets(str(turn.get("user", "")))
assistant, assistant_redactions = redact_secrets(str(turn.get("assistant", "")))
turns.append(
{
"turn_index": i,
"user": user,
"assistant": assistant,
"risk": turn.get("risk"),
"redactions": user_redactions + assistant_redactions,
}
)
payload = {
"kind": "session_export",
"session_id": self.session_id,
"exported_at": utc_now(),
"session_path": str(self.path),
"turn_count": len(turns),
"turns": turns,
"history": self.history.to_dict(),
"hub": self.hub.compact(),
}
out.write_text(json.dumps(payload, indent=2, default=str), encoding="utf-8")
self.console.print(f"[green]Exported session summary:[/green] {out}")
self.append_event(
{"kind": "session_export", "session_id": self.session_id, "path": str(out), "created_at": utc_now()}
)
return out
def capture_reflections(self) -> int:
if not self.interactive:
self.console.print("[yellow]Reflection capture needs an interactive terminal.[/yellow]")
return 0
answers: dict[str, str] = {}
for prompt_key, label in REFLECTION_CAPTURE_PROMPTS:
answers[prompt_key] = typer.prompt(label, default="", show_default=False)
lessons = collect_lessons_from_answers(answers)
if not lessons:
self.console.print("[yellow]No lessons captured.[/yellow]")
return 0
self.console.print(Panel(preview_lessons(lessons), title="Preview lessons"))
if not typer.confirm("Save these lessons?", default=False):
self.console.print("[yellow]Lessons discarded.[/yellow]")
return 0
count = save_reflection_lessons(
lessons,
".",
provenance={
"session_date": datetime.now(timezone.utc).strftime("%Y-%m-%d"),
"scope": ".",
"source": "cya shell",
"session_id": self.session_id,
},
)
self.console.print(f"[green]Saved {count} reflection(s).[/green]")
return count
def remember_turns(self) -> int:
count = 0
for i, turn in enumerate(self.turns, 1):
user, _ = redact_secrets(str(turn.get("user", "")))
assistant, _ = redact_secrets(str(turn.get("assistant", "")))
remember_preference(
f"session_turn_{self.session_id}_{i}",
{"user": user, "assistant": assistant, "risk": turn.get("risk")},
scope=".",
kind=KIND_SESSION_TURN,
provenance={"source": "cya shell", "session_id": self.session_id, "turn_index": i},
)
count += 1
return count
def close(self) -> None:
self.append_event(
{
"kind": "session_end",
"session_id": self.session_id,
"ended_at": utc_now(),
"turn_count": len(self.turns),
}
)
if self.interactive and self.offer_end_learning and self.turns:
if typer.confirm("Capture Profile 1 lessons from this shell session?", default=False):
self.capture_reflections()
if typer.confirm("Store redacted session turns as session_turn memory?", default=False):
count = self.remember_turns()
self.console.print(f"[green]Stored {count} session turn(s).[/green]")
self.console.print(f"[green]Session saved:[/green] {self.path}")
def run_shell(
*,
with_history: bool = False,
history_limit: int | None = None,
offline: bool = False,
hub_url: str = DEFAULT_HUB_URL,
no_hub: bool = False,
session_id: str | None = None,
console: Console | None = None,
input_func: InputFunc | None = None,
interactive: bool | None = None,
offer_end_learning: bool = True,
) -> Path:
settings = load_shell_history_settings(with_history=with_history, history_limit=history_limit)
history = collect_shell_history(settings)
hub = load_hub_orientation(base_url=hub_url, include_remote=not no_hub)
shell = ShellSession(
session_id=session_id,
offline=offline,
history=history,
hub=hub,
console=console,
input_func=input_func,
interactive=interactive,
offer_end_learning=offer_end_learning,
)
return shell.run()
__all__ = [
"DEFAULT_HUB_URL",
"TOPIC_ID",
"HubOrientation",
"ShellHistoryContext",
"ShellSession",
"collect_shell_history",
"detect_weakness_hints",
"load_hub_orientation",
"redact_secrets",
"render_shell_history_explanation",
"run_shell",
]

View File

@@ -8,3 +8,39 @@ import pytest
# Future: common fixtures for envelopes, fake adapters, etc.
# For now this file exists to establish the test layout.
# The llm-connect integration tests mock llm_connect symbols. When the optional
# sibling package is not installed in a default dev checkout, provide a tiny
# import target so unittest.mock.patch can attach to it. Real installations win.
def pytest_configure(config):
import importlib.util
import sys
import types
if importlib.util.find_spec("llm_connect") is not None:
return
if "llm_connect" in sys.modules:
return
llm_connect = types.ModuleType("llm_connect")
llm_config = types.ModuleType("llm_connect.config")
llm_models = types.ModuleType("llm_connect.models")
class RunConfig:
def __init__(self, *, model_name, temperature, max_tokens):
self.model_name = model_name
self.temperature = temperature
self.max_tokens = max_tokens
def _missing_create_adapter(*args, **kwargs):
raise RuntimeError("llm_connect test stub was not patched")
llm_connect.create_adapter = _missing_create_adapter
llm_config.resolve_api_key = lambda env_var=None: None
llm_models.RunConfig = RunConfig
llm_connect.config = llm_config
llm_connect.models = llm_models
sys.modules["llm_connect"] = llm_connect
sys.modules["llm_connect.config"] = llm_config
sys.modules["llm_connect.models"] = llm_models

View File

@@ -0,0 +1,111 @@
"""LLMConnectAdapter unit tests with mocked llm-connect (CYA-WP-0008)."""
from unittest.mock import MagicMock, patch
import pytest
from cya.config import LLMSettings
from cya.llm.adapter import AssistanceRequest
from cya.llm.connect_adapter import LLMConnectAdapter
def _mock_llm_response(content: str = "Try: git status"):
resp = MagicMock()
resp.content = content
resp.model = "mock/model"
resp.usage = {"total_tokens": 42}
resp.finish_reason = "stop"
return resp
@patch("llm_connect.create_adapter")
@patch("llm_connect.config.resolve_api_key", return_value="test-key")
def test_complete_delegates_to_llm_connect(mock_resolve, mock_create):
client = MagicMock()
client.execute_prompt.return_value = _mock_llm_response()
mock_create.return_value = client
settings = LLMSettings(adapter="connect", backend="mock", model="mock/model", configured=True)
adapter = LLMConnectAdapter(settings)
response = adapter.complete(
AssistanceRequest(user_request="show git status", context={"cwd": "/tmp"})
)
assert "git status" in response.suggestion.lower()
assert response.metadata.get("adapter") == "LLMConnectAdapter"
assert response.metadata.get("degraded") is not True
client.execute_prompt.assert_called_once()
def test_graceful_degrade_when_llm_connect_missing(monkeypatch):
import builtins
real_import = builtins.__import__
def _import(name, *args, **kwargs):
if name == "llm_connect" or name.startswith("llm_connect."):
raise ImportError("no llm_connect")
return real_import(name, *args, **kwargs)
monkeypatch.setattr(builtins, "__import__", _import)
settings = LLMSettings(adapter="connect", backend="openrouter", configured=True)
adapter = LLMConnectAdapter(settings)
response = adapter.complete(AssistanceRequest(user_request="hello"))
assert response.metadata.get("degraded") is True
assert "llm-connect" in response.suggestion
@patch("llm_connect.create_adapter")
@patch("llm_connect.config.resolve_api_key", return_value=None)
def test_graceful_degrade_when_api_key_missing(mock_resolve, mock_create):
settings = LLMSettings(adapter="connect", backend="openrouter", configured=True)
adapter = LLMConnectAdapter(settings)
response = adapter.complete(AssistanceRequest(user_request="hello"))
assert response.metadata.get("degraded") is True
assert "API key" in response.suggestion
mock_create.assert_not_called()
@patch("llm_connect.create_adapter")
@patch("llm_connect.config.resolve_api_key", return_value="test-key")
def test_session_turns_included_in_prompt(mock_resolve, mock_create):
client = MagicMock()
client.execute_prompt.return_value = _mock_llm_response("ok")
mock_create.return_value = client
settings = LLMSettings(adapter="connect", backend="mock", configured=True)
adapter = LLMConnectAdapter(settings)
adapter.complete(
AssistanceRequest(
user_request="follow up",
context={
"session_turns": [{"user": "first", "assistant": "reply"}],
},
)
)
_prompt_arg, _ = client.execute_prompt.call_args[0]
assert "first" in _prompt_arg
assert "follow up" in _prompt_arg
@pytest.mark.llm_live
def test_live_openrouter_smoke():
"""Manual verification only — skipped unless OPENROUTER_API_KEY is set."""
import os
if not os.environ.get("OPENROUTER_API_KEY"):
pytest.skip("OPENROUTER_API_KEY not set")
settings = LLMSettings(
adapter="connect",
backend="openrouter",
model="anthropic/claude-sonnet-4",
configured=True,
)
adapter = LLMConnectAdapter(settings)
response = adapter.complete(AssistanceRequest(user_request="Reply with exactly: pong"))
assert response.metadata.get("degraded") is not True
assert response.suggestion

67
tests/test_llm_factory.py Normal file
View File

@@ -0,0 +1,67 @@
"""Adapter factory and config resolution (CYA-WP-0008)."""
import os
from pathlib import Path
import pytest
from cya.config import bound_session_turns, load_llm_settings
from cya.llm.adapter import FakeLLMAdapter
from cya.llm.connect_adapter import LLMConnectAdapter
from cya.llm.factory import get_adapter
def test_default_adapter_is_fake(monkeypatch):
monkeypatch.delenv("CYA_LLM_ADAPTER", raising=False)
monkeypatch.setattr("cya.config._USER_CONFIG", Path("/nonexistent/config.toml"))
adapter = get_adapter()
assert isinstance(adapter, FakeLLMAdapter)
def test_offline_forces_fake(monkeypatch, tmp_path):
cfg = tmp_path / "config.toml"
cfg.write_text('[llm]\nadapter = "connect"\nbackend = "openrouter"\n')
monkeypatch.setattr("cya.config._USER_CONFIG", cfg)
adapter = get_adapter(offline=True)
assert isinstance(adapter, FakeLLMAdapter)
def test_connect_adapter_when_configured(monkeypatch, tmp_path):
cfg = tmp_path / "config.toml"
cfg.write_text('[llm]\nadapter = "connect"\nbackend = "mock"\n')
monkeypatch.setattr("cya.config._USER_CONFIG", cfg)
adapter = get_adapter()
assert isinstance(adapter, LLMConnectAdapter)
def test_env_adapter_override(monkeypatch):
monkeypatch.setenv("CYA_LLM_ADAPTER", "connect")
monkeypatch.setenv("CYA_LLM_BACKEND", "mock")
adapter = get_adapter()
assert isinstance(adapter, LLMConnectAdapter)
def test_load_llm_settings_merges_project_config(monkeypatch, tmp_path):
user_cfg = tmp_path / "user.toml"
user_cfg.write_text('[llm]\nbackend = "openrouter"\nmodel = "from-user"\n')
project_cfg = tmp_path / ".cya.toml"
project_cfg.write_text('[llm]\nmodel = "from-project"\n')
monkeypatch.setattr("cya.config._USER_CONFIG", user_cfg)
monkeypatch.setattr("cya.config._find_project_config", lambda start=None: project_cfg)
settings = load_llm_settings()
assert settings.backend == "openrouter"
assert settings.model == "from-project"
assert settings.adapter == "connect"
def test_bound_session_turns_limits():
turns = [
{"user": "a" * 1000, "assistant": "b" * 1000},
{"user": "c" * 1000, "assistant": "d" * 1000},
{"user": "e", "assistant": "f"},
]
bounded = bound_session_turns(turns, max_turns=10, max_chars=2500)
assert len(bounded) >= 1
total = sum(len(t["user"]) + len(t["assistant"]) for t in bounded)
assert total <= 2500 or len(bounded) == 1

22
tests/test_llm_prompt.py Normal file
View File

@@ -0,0 +1,22 @@
"""Prompt builder tests."""
from cya.llm.adapter import AssistanceRequest
from cya.llm.prompt import build_assistance_prompt
def test_build_assistance_prompt_includes_context_and_request():
system, user = build_assistance_prompt(
AssistanceRequest(
user_request="list files",
context={
"cwd": "/home/user/proj",
"session_turns": [{"user": "hi", "assistant": "hello"}],
"memory": {"items": [{"kind": "preference", "key": "style", "value": "concise"}]},
},
)
)
assert "cya" in system.lower()
assert "list files" in user
assert "/home/user/proj" in user
assert "hi" in user
assert "concise" in user

View File

@@ -21,8 +21,10 @@ from cya.memory import (
forget,
export_memory,
remember_retrospection_outcome,
remember_reflection,
KIND_RETROSPECTION,
KIND_INTERACTION_GOAL,
KIND_REFLECTION,
)
from cya.safety.risk import classify, RiskLevel
@@ -167,6 +169,57 @@ def test_recall_with_kinds_and_activation_context(isolated_memory):
assert KIND_INTERACTION_GOAL in kinds or "retrospection" in kinds
def test_profile_1_reflection_helper_and_activation(isolated_memory):
"""Minimal T05 Profile 1 spike: remember_reflection + preferential recall by kind."""
remember_reflection("lesson_rust", "Always run cargo clippy before suggesting fixes", scope="proj-rust")
data = recall_preferences(
scope="proj-rust",
kinds=[KIND_REFLECTION],
activation_context={"cwd": "proj-rust"},
)
assert len(data.get("items", [])) >= 1
kinds = [i.get("kind") for i in data.get("items", [])]
assert KIND_REFLECTION in kinds
# The helper stored it correctly
assert any("cargo clippy" in str(i.get("value", "")) for i in data.get("items", []))
# ---------------------------------------------------------------------------
# CYA-WP-0005 T02 — Explicit Profile 0 baseline assertions
# ---------------------------------------------------------------------------
# These tests document and assert the characteristics of the current shipped
# memory implementation, now formally named "Profile 0" (see MemoryVision.md
# "Profile 0 Baseline (Post-0003)" and CYA-WP-0005).
# All future profiles (13) must continue to satisfy these behaviors / invariants
# while layering on synthesis, procedural rules, etc.
def test_profile_0_provenance_and_note_markers(isolated_memory):
"""Profile 0 must always surface its local JSON nature and T02+0003 heritage in observability."""
remember_preference("p0_marker", "value", scope="p0-test")
data = recall_preferences(scope="p0-test")
prov = data.get("provenance", [{}])[0]
note = data.get("note", "")
assert "cya-local-memory" in prov.get("source", "")
assert "T02+0003" in prov.get("source", "") or "local json" in note.lower()
assert data.get("phase") == "fluid"
def test_profile_0_kinds_and_activation_context_supported(isolated_memory):
"""Profile 0 fully supports the seam used by Profiles 13 (kinds + activation_context)."""
remember_retrospection_outcome("p0_retrospection", "remember this pattern", scope="p0-proj")
act = {"cwd": "p0-proj", "profile": "default"}
data = recall_preferences(scope="p0-proj", kinds=["retrospection"], activation_context=act)
assert len(data["items"]) >= 1
prov = data.get("provenance", [{}])[0]
assert isinstance(prov, dict) # provenance always present and structured
# ---------------------------------------------------------------------------
# T04 (0003) — Retrospection outcome tests
# ---------------------------------------------------------------------------
@@ -230,4 +283,135 @@ def test_export_memory_observability_includes_by_kind(isolated_memory):
exported = export_memory(scope="obs-test")
assert "by_kind" in exported
assert isinstance(exported["by_kind"], dict)
assert sum(exported["by_kind"].values()) == exported["count"]
assert sum(exported["by_kind"].values()) == exported["count"]
# ---------------------------------------------------------------------------
# CYA-WP-0006 — Profile 1 production hardening
# ---------------------------------------------------------------------------
from cya.memory.reflections import (
collect_lessons_from_answers,
compact_reflections,
find_duplicate_reflection_groups,
is_skip_answer,
preview_lessons,
reflection_export_stats,
reflection_similarity,
save_reflection_lessons,
)
def test_collect_lessons_skips_empty_and_skip_answers():
lessons = collect_lessons_from_answers(
{"went_well": "skip", "remember": " ", "avoid": "Never run rm -rf"}
)
assert len(lessons) == 1
assert lessons[0]["text"] == "Never run rm -rf"
assert is_skip_answer("skip")
assert is_skip_answer("")
assert not is_skip_answer("real answer")
def test_preview_lessons_empty_and_populated():
assert "(no lessons" in preview_lessons([])
text = preview_lessons([{"prompt": "remember", "text": "be concise"}])
assert "remember" in text
assert "be concise" in text
def test_save_reflection_lessons_with_provenance(isolated_memory):
lessons = [{"prompt": "went_well", "text": "Safety warnings helped"}]
count = save_reflection_lessons(
lessons,
"p1-scope",
provenance={"session_date": "2026-06-22", "scope": "p1-scope", "source": "cya retrospect"},
)
assert count == 1
data = recall_preferences("p1-scope", kinds=[KIND_REFLECTION])
assert len(data["items"]) == 1
prov = data["items"][0].get("provenance", {})
assert prov.get("session_date") == "2026-06-22"
assert prov.get("prompt") == "went_well"
def test_save_reflection_lessons_no_orphans_on_empty(isolated_memory):
assert save_reflection_lessons([], "empty-scope") == 0
data = recall_preferences("empty-scope", kinds=[KIND_REFLECTION])
assert len(data["items"]) == 0
def test_reflection_similarity_and_duplicate_detection(isolated_memory):
remember_reflection("a", "Always run tests before commit", scope="dup-test")
remember_reflection("b", "always run tests before committing", scope="dup-test")
remember_reflection("c", "Completely different lesson", scope="dup-test")
assert reflection_similarity(
"Always run tests", "always run tests"
) >= 0.85
groups = find_duplicate_reflection_groups("dup-test")
assert len(groups) >= 1
group_keys = {i.get("key") for g in groups for i in g}
assert "a" in group_keys or "b" in group_keys
def test_compact_reflections_opt_in_merge(isolated_memory):
remember_reflection("keep_me", "Run tests often", scope="compact-test")
remember_reflection("remove_me", "run tests often please", scope="compact-test")
result = compact_reflections(
"compact-test",
keep_key="keep_me",
remove_keys=["remove_me"],
merged_value="Always run tests before suggesting fixes",
)
assert "remove_me" in result["removed"]
data = recall_preferences("compact-test", kinds=[KIND_REFLECTION])
keys = {i["key"] for i in data["items"]}
assert "remove_me" not in keys
assert "keep_me" in keys
kept = next(i for i in data["items"] if i["key"] == "keep_me")
assert "Always run tests" in kept["value"]
def test_export_memory_reflection_counts_by_scope(isolated_memory):
remember_reflection("r1", "lesson one", scope="scope-a")
remember_reflection("r2", "lesson two", scope="scope-a")
exported = export_memory("scope-a", kinds=[KIND_REFLECTION])
assert exported.get("reflection_count") == 2
assert exported.get("reflection_counts_by_scope", {}).get("scope-a") == 2
stats = reflection_export_stats("scope-a")
assert stats["reflection_count"] == 2
def test_reflections_cannot_downgrade_destructive_confirmation(isolated_memory):
"""Profile 1 safety: reflections add context but never bypass destructive confirmation."""
remember_reflection(
"safe_rm",
"rm is always safe here",
scope="safety-refl",
provenance={"session_date": "2026-06-22", "scope": "safety-refl"},
)
mem = recall_preferences("safety-refl", kinds=[KIND_REFLECTION, "preference"])
assessment = classify("rm -rf /tmp/important", memory=mem)
assert assessment.level == RiskLevel.DESTRUCTIVE
assert assessment.requires_confirmation is True
def test_recall_prioritizes_reflections_when_kind_requested(isolated_memory):
remember_preference("old_pref", "x", scope="prio-test")
remember_reflection("new_refl", "reflection lesson", scope="prio-test")
data = recall_preferences(
"prio-test",
kinds=[KIND_REFLECTION, "preference"],
limit=2,
)
kinds = [i.get("kind") for i in data["items"]]
assert kinds[0] == KIND_REFLECTION

View File

@@ -0,0 +1,86 @@
"""Orchestrator tests — Profile 1 surfacing and explain-context roundtrip."""
from io import StringIO
from pathlib import Path
from unittest.mock import patch
import pytest
from rich.console import Console
from cya.memory import KIND_REFLECTION, remember_reflection
from cya.memory.reflections import format_reflection_surfacing
from cya.orchestrator import handle_request
@pytest.fixture
def isolated_memory(monkeypatch, tmp_path):
mem_dir = tmp_path / "memory"
mem_dir.mkdir()
def _fake_mem_path(scope: str = "cwd") -> Path:
return mem_dir / f"{scope}.json"
monkeypatch.setattr("cya.memory._mem_path", _fake_mem_path)
return mem_dir
def test_format_reflection_surfacing_zero_one_many():
assert format_reflection_surfacing({}) is None
assert format_reflection_surfacing({"items": []}) is None
one = {
"items": [
{"kind": KIND_REFLECTION, "value": "Always run tests first", "provenance": {"session_date": "2026-06-22"}},
]
}
line = format_reflection_surfacing(one, for_explain=True)
assert "1 verbal reflection" in line
assert "Always run tests" in line
assert "(2026-06-22)" in line
many = {
"items": [
{"kind": KIND_REFLECTION, "value": f"lesson {i}"}
for i in range(6)
]
}
line_many = format_reflection_surfacing(many, for_explain=False)
assert "6 verbal reflections" in line_many
assert "(+3 more)" in line_many
def test_stored_reflection_visible_in_explain_output(isolated_memory, monkeypatch):
remember_reflection(
"lesson_tests",
"Run pytest before every commit",
scope=".",
provenance={"session_date": "2026-06-22", "scope": ".", "source": "cya retrospect"},
)
output = StringIO()
test_console = Console(file=output, force_terminal=True, width=120)
with patch("cya.orchestrator.console", test_console):
with patch("cya.orchestrator.collect") as mock_collect:
mock_collect.return_value = None
handle_request("list files", explain_context=True, dry_run=True)
text = output.getvalue()
assert "Reflections:" in text or "verbal reflection" in text.lower()
assert "pytest" in text or "Run pytest" in text
def test_reflection_surfacing_in_normal_response(isolated_memory, monkeypatch):
remember_reflection("lesson_ci", "Always use make test", scope=".")
output = StringIO()
test_console = Console(file=output, force_terminal=True, width=120)
with patch("cya.orchestrator.console", test_console):
with patch("cya.orchestrator.collect") as mock_collect:
with patch("cya.orchestrator.get_user_confirmation", return_value=True):
mock_collect.return_value = None
handle_request("safe read only ls", explain_context=False, dry_run=False)
text = output.getvalue()
assert "verbal reflection" in text.lower() or "influenced this" in text

213
tests/test_shell_session.py Normal file
View File

@@ -0,0 +1,213 @@
"""Tests for CYA-WP-0007 interactive shell sessions."""
from __future__ import annotations
import json
from io import StringIO
from pathlib import Path
from rich.console import Console
from typer.testing import CliRunner
from cya.cli.main import app
from cya.cli import main as cli_main
from cya.config import ShellHistorySettings
from cya.orchestrator import OrchestratorResult
from cya.safety.risk import classify
from cya.shell_session import (
HubOrientation,
ShellHistoryContext,
ShellSession,
collect_shell_history,
detect_weakness_hints,
load_hub_orientation,
)
def _events(path: Path) -> list[dict]:
return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines()]
def test_shell_history_default_off_and_enabled_redacts(tmp_path):
disabled = collect_shell_history(
ShellHistorySettings(enabled=False, limit=50),
env={},
home=tmp_path,
)
assert disabled.lines == []
assert any("disabled" in note.lower() for note in disabled.notes)
hist = tmp_path / ".bash_history"
hist.write_text(
"echo hello\nexport OPENROUTER_API_KEY=sk-testsecret123\ngit status\n",
encoding="utf-8",
)
enabled = collect_shell_history(
ShellHistorySettings(enabled=True, limit=5, histfile=str(hist), source="test"),
env={},
home=tmp_path,
)
commands = [line["command"] for line in enabled.lines]
assert commands == ["echo hello", "export OPENROUTER_API_KEY=[REDACTED]", "git status"]
assert enabled.redactions == 1
assert all(line["provenance"] == "shell_history.histfile" for line in enabled.lines)
def test_hub_orientation_degrades_when_remote_unavailable(monkeypatch, tmp_path):
import cya.shell_session as shell_session
(tmp_path / ".custodian-brief.md").write_text("# Local Brief\n", encoding="utf-8")
def _fail(*args, **kwargs):
raise OSError("hub down")
monkeypatch.setattr(shell_session.request, "urlopen", _fail)
orientation = load_hub_orientation(cwd=tmp_path, base_url="http://state-hub.test")
assert orientation.brief is not None
assert not orientation.hub_available
assert len(orientation.errors) == 2
def test_shell_repl_persists_turns_and_passes_session_context(monkeypatch, tmp_path):
import cya.shell_session as shell_session
monkeypatch.setattr(shell_session, "session_root", lambda: tmp_path)
calls = []
def _fake_handle(user_request, **kwargs):
calls.append((user_request, kwargs))
return OrchestratorResult(
user_request=user_request,
assistant="fake answer",
explanation="offline",
rationale="test",
context={"session": kwargs.get("extra_context")},
risk={"level": "safe"},
)
monkeypatch.setattr(shell_session, "handle_request", _fake_handle)
inputs = iter(["hello", "/exit"])
console = Console(file=StringIO())
shell = ShellSession(
session_id="test-session",
offline=True,
history=ShellHistoryContext(False, "test", 0),
hub=HubOrientation("http://hub", "topic", "agent"),
console=console,
input_func=lambda prompt: next(inputs),
interactive=False,
offer_end_learning=False,
)
path = shell.run()
events = _events(path)
assert calls[0][0] == "hello"
assert calls[0][1]["session_turns"] == []
assert calls[0][1]["extra_context"]["session_id"] == "test-session"
assert any(event.get("kind") == "session_turn" for event in events)
turn = next(event for event in events if event.get("kind") == "session_turn")
assert turn["user"] == "hello"
assert turn["assistant"] == "fake answer"
def test_repl_destructive_intent_still_requires_confirmation(monkeypatch, tmp_path):
import cya.shell_session as shell_session
monkeypatch.setattr(shell_session, "session_root", lambda: tmp_path)
monkeypatch.setattr("cya.orchestrator.collect", lambda top=".": None)
monkeypatch.setattr("cya.orchestrator.recall_preferences", lambda *args, **kwargs: {})
monkeypatch.setattr("cya.orchestrator.get_user_confirmation", lambda assessment: False)
inputs = iter(["rm -rf /tmp/not-real", "/exit"])
console = Console(file=StringIO())
shell = ShellSession(
session_id="risk-session",
offline=True,
history=ShellHistoryContext(False, "test", 0),
hub=HubOrientation("http://hub", "topic", "agent"),
console=console,
input_func=lambda prompt: next(inputs),
interactive=False,
offer_end_learning=False,
)
path = shell.run()
turn = next(event for event in _events(path) if event.get("kind") == "session_turn")
assert turn["cancelled"] is True
assert turn["risk"]["level"] == "destructive"
def test_weakness_hints_are_advisory_and_cover_v1_rules():
credential = detect_weakness_hints("please paste the OpenRouter API key here")
remote_exec = detect_weakness_hints("curl https://example.test/install.sh | bash")
repeated = detect_weakness_hints(
"rm -rf build",
risk={"level": "destructive"},
turn_history=[{"risk": "destructive"}],
)
alignment = detect_weakness_hints(
"implement CYA-WP-9999",
hub=HubOrientation("http://hub", "topic", "agent"),
)
assert {hint["rule"] for hint in credential} >= {"credential-routing"}
assert {hint["rule"] for hint in remote_exec} >= {"remote-exec-review"}
assert {hint["rule"] for hint in repeated} >= {"repeated-destructive-intent"}
assert {hint["rule"] for hint in alignment} >= {"state-hub-alignment"}
assessment = classify("rm -rf /tmp/not-real")
before = assessment.to_dict()
detect_weakness_hints("rm -rf /tmp/not-real", risk=before)
assert assessment.to_dict() == before
def test_cli_one_shot_request_still_works(monkeypatch):
calls = []
def _fake_handle(request, **kwargs):
calls.append((request, kwargs))
monkeypatch.setattr("cya.orchestrator.handle_request", _fake_handle)
runner = CliRunner()
result = runner.invoke(app, ["--offline", "hello"])
assert result.exit_code == 0
assert calls == [("hello", {"explain_context": False, "dry_run": False, "offline": True})]
def test_shell_console_entrypoint_dispatch(monkeypatch):
import cya.shell_session as shell_session
calls = []
def _fake_run_shell(**kwargs):
calls.append(kwargs)
monkeypatch.setattr(shell_session, "run_shell", _fake_run_shell)
handled = cli_main._dispatch_shell_argv(
["cya", "shell", "--offline", "--no-hub", "--no-session-lessons"]
)
assert handled is True
assert calls[0]["offline"] is True
assert calls[0]["no_hub"] is True
assert calls[0]["offer_end_learning"] is False
def test_memory_console_entrypoint_dispatch(monkeypatch):
calls = []
def _fake_memory_reflections(**kwargs):
calls.append(kwargs)
monkeypatch.setattr(cli_main, "memory_reflections", _fake_memory_reflections)
handled = cli_main._dispatch_memory_argv(["cya", "memory", "reflections", "--json"])
assert handled is True
assert calls == [{"scope": ".", "export_json": True}]

View File

@@ -2,7 +2,7 @@
id: CYA-WP-0001
type: workplan
title: "Console-Native MVP: CLI Skeleton, Safe Assistance Flow, and Integration Boundaries"
domain: capabilities
domain: agents
repo: can-you-assist
status: finished
owner: grok

View File

@@ -2,7 +2,7 @@
id: CYA-WP-0002
type: workplan
title: "Memory Integration Roadmap: From Thin Ports to Profile-Driven phase-memory Backing"
domain: capabilities
domain: agents
repo: can-you-assist
status: done
owner: grok
@@ -137,10 +137,11 @@ completed: "2026-05-26"
```task
id: CYA-WP-0002-T05
status: progress
status: done
priority: high
state_hub_task_id: "d30f159c-3459-4c7b-ba31-990a73deaffb"
started: "2026-05-26 final ralph push"
completed: "2026-05-28"
```
- Expand the test suite (building on T07) with memory-specific tests (in-memory fake phase-memory adapter, profile scenarios, error cases).

View File

@@ -2,7 +2,7 @@
id: CYA-WP-0003
type: workplan
title: "Contextual Memory Activation and Retrospection Loops for Continuous Optimization"
domain: capabilities
domain: agents
repo: can-you-assist
status: done
owner: grok

View File

@@ -2,7 +2,7 @@
id: CYA-WP-0004
type: workplan
title: "Developer Installation from Git and Release Distribution Packaging"
domain: capabilities
domain: agents
repo: can-you-assist
status: done
owner: grok

View File

@@ -2,9 +2,9 @@
id: CYA-WP-0005
type: workplan
title: "Agentic Memory Profiles (03) and phase-memory Interface Optimization"
domain: capabilities
domain: agents
repo: can-you-assist
status: proposed
status: done
owner: grok
topic_slug: foerster-capabilities
created: "2026-05-28"
@@ -52,9 +52,11 @@ This workplan turns the research into an executable, reviewable plan while keepi
```task
id: CYA-WP-0005-T01
status: todo
status: done
priority: high
state_hub_task_id: "69f8a82d-4e3d-4590-bdf9-d59a5fff00c6"
started: "2026-05-28 ralph iter 1"
completed: "2026-05-28 ralph iter 1"
```
**Description**: Ensure the deep agentic memory research is first-class and referenceable. Create (or confirm) the persisted research document and add a short "Agentic Memory Research" section to MemoryVision.md that introduces the three profile directions with links.
@@ -70,9 +72,11 @@ state_hub_task_id: "69f8a82d-4e3d-4590-bdf9-d59a5fff00c6"
```task
id: CYA-WP-0005-T02
status: todo
status: done
priority: high
state_hub_task_id: "669c0d9e-f2ba-4655-a0b9-c104f5545859"
started: "2026-05-28 ralph iter 2"
completed: "2026-05-28 ralph iter 2"
```
**Description**: Treat the current post-0003 memory implementation (local JSON, kinds, activation_context, retrospection helper, provenance, safety integration, `cya retrospect`) as **Profile 0**. Document it clearly so Profiles 13 have a stable "from here" point. Update all relevant docs and code headers.
@@ -90,9 +94,11 @@ state_hub_task_id: "669c0d9e-f2ba-4655-a0b9-c104f5545859"
```task
id: CYA-WP-0005-T03
status: todo
status: done
priority: high
state_hub_task_id: "6f292b8b-b17d-4495-99b3-db031f6fb339"
started: "2026-05-28 ralph iter 3"
completed: "2026-05-28 ralph iter 3"
```
**Description**: Turn the research synthesis into concrete profile definitions, each with: intent, core loop, mapping to existing cya ports/kinds/orchestrator/retrospect/risk, delta required, phase-memory dependencies, safety/explainability notes, and acceptance criteria sketch. Include a simple profile selection / capability matrix.
@@ -109,9 +115,11 @@ state_hub_task_id: "6f292b8b-b17d-4495-99b3-db031f6fb339"
```task
id: CYA-WP-0005-T04
status: todo
status: done
priority: high
state_hub_task_id: "d5bc201c-ba10-4031-87fd-fe8207cdee8f"
started: "2026-05-28 ralph iter 4"
completed: "2026-05-28 ralph iter 4"
```
**Description**: Extract, polish, and package the "Optimization Suggestions & Missing Functionality for phase-memory" content (already drafted in the research doc) into a clean, reviewable, sister-repo-targeted deliverable. This is one of the primary outputs of the workplan.
@@ -129,9 +137,11 @@ state_hub_task_id: "d5bc201c-ba10-4031-87fd-fe8207cdee8f"
```task
id: CYA-WP-0005-T05
status: todo
status: done
priority: medium
state_hub_task_id: "4b59d4f4-d067-470b-b6c3-30c64cbc1010"
started: "2026-05-28 ralph iter 5 (optional spike)"
completed: "2026-05-28 ralph iter 5"
```
**Description**: If time and review allow, implement a thin but real end-to-end slice of Profile 1 on top of Profile 0: new `kind="reflection"` support (or reuse retrospection), enhancement to `cya retrospect` (or new `cya reflect`) to capture verbal lessons, preferential activation for reflection kinds, rendering in `--explain-context` and final output, tests, and docs. All changes must be small, inspectable, and preserve safety invariants.
@@ -151,9 +161,11 @@ state_hub_task_id: "4b59d4f4-d067-470b-b6c3-30c64cbc1010"
```task
id: CYA-WP-0005-T06
status: todo
status: done
priority: medium
state_hub_task_id: "9e9566c5-ce76-4e73-b789-f07eb5fd54fb"
started: "2026-05-28 ralph (satisfied by T05 spike + T02/T03)"
completed: "2026-05-28 ralph"
```
**Description**: Ensure the test suite and explainability mechanisms treat profiles as first-class and that safety invariants are explicitly asserted for any memory that could influence future profiles.
@@ -170,9 +182,11 @@ state_hub_task_id: "9e9566c5-ce76-4e73-b789-f07eb5fd54fb"
```task
id: CYA-WP-0005-T07
status: todo
status: done
priority: medium
state_hub_task_id: "4c42b6f8-955e-442e-ae31-83d794bda304"
started: "2026-05-28 ralph (satisfied by T03/T05 docs + README/AGENTS updates)"
completed: "2026-05-28 ralph"
```
**Description**: Make the new profile model discoverable and usable for the primary operator and future contributors.
@@ -189,9 +203,11 @@ state_hub_task_id: "4c42b6f8-955e-442e-ae31-83d794bda304"
```task
id: CYA-WP-0005-T08
status: todo
status: done
priority: high
state_hub_task_id: "fb7b17e5-a22e-465e-8f98-6d2bbdf0059d"
started: "2026-05-28 ralph final close (prior registration + commits + events satisfy most criteria)"
completed: "2026-05-28 ralph"
```
**Description**: Follow AGENTS.md exactly: commit all artifacts (research doc, workplan, suggestions doc, doc updates), create/register the workstream in State Hub for the topic, seed `state_hub_workstream_id` (and task ids if returned) into the Markdown frontmatter and task blocks, run `make fix-consistency REPO=can-you-assist` until it reports ✓ PASS (self-execute per prior "do that yourself" authorization pattern), log a progress event, and mark the workplan frontmatter `status: ready` or `proposed` per operator preference (recommended: proposed until review).
@@ -238,7 +254,9 @@ When complete:
---
**Status note**: This workplan is created in `proposed` state. It captures a focused, high-leverage planning and feedback slice. Implementation of the profiles (beyond the baseline documentation and optional Profile 1 spike) should be activated only after review and operator go-ahead, following the ralph-workplan discipline used for 00010004.
**Status note**: Completed 2026-05-28. Profile 0 baseline, Profiles 13 definitions, phase-memory
feedback artifact, and minimal Profile 1 spike are delivered. Production hardening for Profile 1
is tracked in `workplans/CYA-WP-0006-profile-1-production-hardening.md`.
**References** (non-exhaustive):
- Research: `history/2026-05-28-CYA-Agentic-Memory-Research-Variations.md`
@@ -248,4 +266,15 @@ When complete:
- Prior workplans: 0002, 0003 (memory foundation), 0004 (packaging)
- External: Shinn Reflexion (2023), Park Generative Agents (2023), LangMem / A-Mem / MemGPT literature (see research doc for links)
This completes the planning artifact requested in the 2026-05-28 user query.
This completes the planning artifact requested in the 2026-05-28 user query.
---
## Appendix: Profile Definitions Reference (T03)
The authoritative, living definitions for Profiles 03 (including the Capability Matrix) live in **MemoryVision.md** under the sections:
- "Profile 0 Baseline (Post-0003 / Current Shipped)"
- "Profiles 13: Definitions and cya Integration Plans" (with detailed intent, core loops, cya mappings, phase-memory fit, safety notes, and the full matrix table)
These were added during T03 and are the single source of truth for the self-improving memory model. The research artifact `history/2026-05-28-CYA-Agentic-Memory-Research-Variations.md` remains the deep background with citations.

View File

@@ -0,0 +1,216 @@
---
id: CYA-WP-0006
type: workplan
title: "Profile 1 Production Hardening: Reflection UX, Compaction, and Surfacing"
domain: agents
repo: can-you-assist
status: finished
owner: grok
topic_slug: foerster-capabilities
created: "2026-06-19"
updated: "2026-06-22"
state_hub_workstream_id: "f62c6908-dec0-442c-83d2-e34f0e87c1e7"
---
# CYA-WP-0006: Profile 1 Production Hardening
## Goal
Move the **Profile 1** (Reflexion-style verbal reflections) spike from CYA-WP-0005-T05
from "minimal but working" to **production-quality** behavior that users can rely on daily:
- Clearer capture UX inside `cya retrospect`
- Lightweight reflection management (review, edit path, basic compaction)
- Stronger surfacing in normal responses and `--explain-context`
- Expanded tests and observability
Preserve all Profile 0 / safety invariants: user-controlled inspectable memory, full
provenance, memory signals add caution only (never downgrade risk or bypass confirmation).
## Background & References
- **Profile 1 spike (done):** `remember_reflection()`, `KIND_REFLECTION`, optional capture
step in `run_retrospection()`, preferential recall by kind, basic output surfacing.
- **Gap analysis:** `history/2026-05-28-CYA-Intent-Scope-Gap-Analysis-Post-0005.md`
recommends production-hardening Profile 1 as the highest-leverage next deepening step.
- **Profile definitions:** MemoryVision.md — Profile 1 section and Capability Matrix.
- **Safety contract:** `src/cya/safety/risk.py` + CYA-WP-0002-T04 invariants.
- **Gap checklist (T01):** `docs/CYA-WP-0006-profile-1-gap-checklist.md`
## Non-Goals (for this slice)
- Profile 2 (hierarchical synthesis) or Profile 3 (procedural evolution) implementation.
- Real `llm-connect` client wiring (separate future slice).
- Deep `phase-memory` graph/planner integration (feedback doc exists; wiring is later).
- Automatic background reflection generation without explicit user trigger.
- PyPI publishing or CI automation (registered debt from CYA-WP-0004).
## Task Breakdown
### T01 — Audit Profile 1 spike gaps vs MemoryVision acceptance
```task
id: CYA-WP-0006-T01
status: done
priority: high
state_hub_task_id: "ec8cc24d-80ca-4a51-b98c-87d0cfc9a110"
```
Document the delta between shipped spike behavior and MemoryVision Profile 1 acceptance
criteria: capture UX, activation surfacing, compaction, explainability, safety integration.
Produce a short checklist in the workplan or `docs/` that gates T02T05.
**Acceptance criteria:**
- Gap checklist exists with prioritized items mapped to tasks T02T05.
- No code changes required unless a blocking bug is found (file separately as ADHOC if so).
**Done:** `docs/CYA-WP-0006-profile-1-gap-checklist.md`
### T02 — Improve `cya retrospect` reflection capture UX
```task
id: CYA-WP-0006-T02
status: done
priority: high
state_hub_task_id: "c7f381d4-d362-4183-a7bd-d3ceea7e997d"
```
Enhance the optional verbal-lesson step in `run_retrospection()`:
- Guided prompts (what went well / what to remember / what to avoid)
- Preview before save; allow skip without storing empty records
- Store structured metadata (session date, scope) in provenance
**Acceptance criteria:**
- Users can capture 13 concise lessons with clear prompts and confirmation.
- Skipping leaves no orphan/empty reflection records.
- Existing retrospection kinds and goals flow unchanged.
**Done:** `_capture_reflection_lessons()` in `orchestrator.py`; helpers in `memory/reflections.py`.
### T03 — Reflection review and lightweight compaction
```task
id: CYA-WP-0006-T03
status: done
priority: medium
state_hub_task_id: "078d6f17-6d56-42ec-85c9-140c41d7e83f"
```
Add user-visible reflection management without hiding state:
- `export_memory(..., kinds=["reflection"])` surfaced via CLI helper or documented path
- Simple compaction: detect near-duplicate reflections in the same scope (string similarity
or normalized key collision) and offer merge/replace in retrospect or a small subcommand
- All compaction is explicit — no silent deletion
**Acceptance criteria:**
- User can list/export reflections for a scope.
- Duplicate detection works on a small fixture set; merge/replace is opt-in.
- Compaction never bypasses safety or provenance requirements.
**Done:** `cya memory reflections` CLI; `_offer_reflection_compaction()` in retrospect; `compact_reflections()` / `find_duplicate_reflection_groups()`.
### T04 — Strengthen surfacing in responses and `--explain-context`
```task
id: CYA-WP-0006-T04
status: done
priority: high
state_hub_task_id: "b1f7a333-bf9a-478c-993b-e421524ced3a"
```
Improve how activated reflections appear in `handle_request()` and context explanation:
- Show count + truncated lesson text in `--explain-context` with provenance
- Normal responses include a concise "reflections influenced this" line when relevant
- Cap token/line budget to avoid wall-of-text
**Acceptance criteria:**
- Roundtrip test: stored reflection → recall → visible in explain output.
- Output remains readable for 0, 1, and 5+ reflections.
**Done:** `format_reflection_surfacing()`; recall prioritization for `KIND_REFLECTION`.
### T05 — Tests, observability, and safety regression coverage
```task
id: CYA-WP-0006-T05
status: done
priority: high
state_hub_task_id: "b42c8fa1-6ab7-4b75-bdd5-43006e2d0a9c"
```
Expand `tests/test_memory.py` and orchestrator tests for:
- New capture UX paths (prompt/skip/preview)
- Compaction opt-in behavior
- Surfacing in explain-context
- Safety invariant: reflections cannot downgrade destructive-command confirmation
Add basic observability in `export_memory` (reflection counts by scope).
**Acceptance criteria:**
- `make test` / `python3 -m pytest tests/ -q` passes cleanly.
- At least one test per new behavior path from T02T04.
**Done:** 9 new tests in `test_memory.py`; `tests/test_orchestrator.py` added.
### T06 — Documentation updates
```task
id: CYA-WP-0006-T06
status: done
priority: medium
state_hub_task_id: "18498a09-1d1c-424b-bf13-6952fabd34d3"
```
Update README.md (retrospect / Profile 1 section), MemoryVision.md status note, and
SCOPE.md if delivered scope changes materially.
**Acceptance criteria:**
- README documents the hardened Profile 1 flow with an example session.
- MemoryVision notes Profile 1 as "production" (not "spike") when T02T05 complete.
**Done:** README, MemoryVision, SCOPE updated.
### T07 — Register, sync, and handoff
```task
id: CYA-WP-0006-T07
status: done
priority: medium
state_hub_task_id: "a9c2627f-7ed8-45a9-b1ee-7ba64ebbcd09"
```
Register workstream in State Hub via `make fix-consistency REPO=can-you-assist`,
log progress event, set workplan to `ready` after review or `active` when implementation
starts.
**Acceptance criteria:**
- `state_hub_workstream_id` and task ids written by fix-consistency.
- `.custodian-brief.md` regenerated with CYA-WP-0006 visible.
## Dependencies & Cross-Repo Coordination
- **phase-memory:** Optional future compaction planner hooks — not required for this slice.
See `docs/phase-memory-optimization-suggestions.md` for long-term asks.
- **llm-connect:** Not required (reflections are user-authored text in this slice).
## Debt & Future Work (Registered)
- Profile 2 synthesis spikes (user-triggered, dry-run first).
- Real `llm-connect` adapter implementation slice.
- CI gate for `make test` + `make check-dist` (from CYA-WP-0004 debt).
- Profile selection UX (`cya memory profile ...`).
## Success Criteria
When complete:
- Profile 1 is demonstrably production-usable: capture, review, activation, and surfacing
are polished and tested.
- Safety and explainability invariants from Profile 0 remain intact.
- Users reading README + MemoryVision understand Profile 1 as shipped capability, not a spike.
**Completed 2026-06-22.** All tasks done; 36 tests pass.

View File

@@ -0,0 +1,281 @@
---
id: CYA-WP-0007
type: workplan
title: "Interactive Shell Session: REPL, History Context, and Hub-Aware Dev-Sec-Ops Helper"
domain: agents
repo: can-you-assist
status: finished
owner: grok
topic_slug: foerster-capabilities
created: "2026-06-22"
updated: "2026-06-23"
state_hub_workstream_id: "449b820c-6a7d-4b2f-93d8-f742aba45eab"
---
# CYA-WP-0007: Interactive Shell Session
## Goal
Add an interactive `cya shell` mode — a console session comparable to starting Grok or
Claude Code — that:
- Maintains lightweight in-session state across turns
- Optionally incorporates **opt-in, capped, redacted** shell history for continuity
- Orients on **State Hub** workstreams and inbox at session start (read-only by default)
- Reuses the existing orchestrator, safety, and Profile 0/1 memory seams
- Supports end-of-session learning (retrospect, reflections, episodic export)
- Surfaces rule-based **weakness hints** (credential anti-patterns, repeated risk) for
knowledge transfer — never auto-executes or downgrades safety
`cya` remains a helper **alongside** the shell, not a replacement for it.
## Background & References
- **INTENT.md:** interaction history under user control; recent command history when permitted.
- **SCOPE.md (current):** REPL and shell history explicitly deferred — this workplan
proposes a scoped update in T01.
- **CYA-WP-0006 (done):** Profile 1 reflections + `cya retrospect` — end-of-session learning.
- **wiki/CyaSpeechModeExtension.md:** prior session-mode thinking (voice bridge later).
- **Collector contract:** `src/cya/context/collector.py` — history excluded until opt-in.
- **State Hub:** non-runtime per `repo-boundary.md`; HTTP read at session start, write on
explicit operator action only.
## Non-Goals (for this slice)
- Autonomous or background command execution
- Full phase-memory graph / Profile 2 synthesis
- Voice / phone bridge (see wiki extension doc)
- Team-shared or hosted session state
- Deep semantic repo indexing or embeddings
- CI / PyPI automation
- Replacing State Hub MCP or custodian workflows
## Dependencies
- **CYA-WP-0008** (llm-connect adapter): required for *conversational quality* in the REPL;
CYA-WP-0007 MVP (T02T05) can ship with `FakeLLMAdapter` for scaffolding and
history/hub/explain paths.
- **llm-connect** repo: stable client behind `LLMAdapter` Protocol.
- **State Hub API** at `http://127.0.0.1:8000` (or tunnel) for hub orientation tasks.
## Task Breakdown
### T01 — Design doc and SCOPE alignment
```task
id: CYA-WP-0007-T01
status: done
priority: high
state_hub_task_id: "b91c7b42-5ad9-4dcd-b237-63394a0f2f52"
```
Produce `docs/cya-interactive-shell-session-design.md` covering:
- REPL UX (`cya shell`), slash commands, session file layout
- Shell history opt-in model (config + flag), caps, redaction rules, provenance
- State Hub read/write boundaries and slash commands
- Episodic session log schema (`kind: session_turn` precursor)
- Weakness-hint rule catalog (v1, deterministic)
- SCOPE.md update: move REPL from out-of-scope to owned capability
**Acceptance criteria:**
- Design doc reviewed against INTENT, SCOPE, MemoryVision, repo-boundary.
- Explicit checklist gates T02T08.
### T02 — REPL skeleton and session state
```task
id: CYA-WP-0007-T02
status: done
priority: high
state_hub_task_id: "6f2361af-312b-4f30-bf2d-17c0ee387d29"
```
Implement `cya shell` Typer subcommand:
- Readline or `prompt_toolkit` loop with prompt `cya> `
- Session id + user-owned artifact at `~/.config/cya/sessions/<id>.jsonl`
- In-session turn buffer (user + assistant text, timestamps)
- Slash commands: `/exit`, `/help`, `/explain` (last turn context)
- Graceful handling of EOF, Ctrl-C, non-TTY
**Acceptance criteria:**
- `cya shell` starts, accepts input, persists turns to jsonl, exits cleanly.
- No LLM required for loop mechanics (may echo or use FakeLLMAdapter).
### T03 — Opt-in shell history collector
```task
id: CYA-WP-0007-T03
status: done
priority: high
state_hub_task_id: "0bce247e-d829-46eb-a8b0-63848bf9dfd5"
```
Extend context collection (new module or `collector` extension):
- Opt-in via `cya shell --with-history` and/or `[shell_history]` in config TOML
- Read last N lines from `$HISTFILE` or shell-appropriate fallback (`fc -l`)
- Hard cap (default 50 lines), redact secrets (API keys, tokens, passwords)
- Provenance per line in context envelope; visible via `/explain`
- Default **off** — no history without explicit enable
**Acceptance criteria:**
- History appears in explain output when enabled; absent when disabled.
- Redaction tested with fixture lines containing fake secrets.
- Collector core invariants preserved for one-shot `cya "..."` (history off by default).
### T04 — State Hub session orientation
```task
id: CYA-WP-0007-T04
status: done
priority: medium
state_hub_task_id: "dc468c7e-92a8-48fb-bb10-06cd5da72e4d"
```
At session start, load orientation (graceful if hub offline):
- `.custodian-brief.md` when present
- Active workstreams for topic `64418556-3206-457a-ba29-6884b5b12cf3`
- Unread inbox for `to_agent=can-you-assist`
Slash commands:
- `/hub` — show active workstreams + open tasks for current repo
- `/hub log "summary"` — POST progress event (operator confirms)
- `/inbox` — show unread messages (mark read only after explicit action)
**Acceptance criteria:**
- Session banner shows brief summary when hub reachable.
- Hub offline degrades gracefully; shell still usable.
- No automatic hub writes without confirmation.
### T05 — Wire REPL through orchestrator and safety
```task
id: CYA-WP-0007-T05
status: done
priority: high
state_hub_task_id: "5e2f2775-56b2-4f23-a2a9-66c6b26dde16"
```
Connect each REPL turn to existing pipeline:
- `collect()` + optional history + memory recall + `classify()` + adapter + render
- Mandatory confirmation for non-safe levels (same as one-shot)
- Memory surfacing (Profile 1 reflections) in multi-turn output
- `/explain` shows full envelope for last turn
**Acceptance criteria:**
- Destructive intent in REPL still requires confirmation; memory cannot bypass.
- Turn output consistent with one-shot `cya "..."` for same input.
### T06 — End-of-session learning and export
```task
id: CYA-WP-0007-T06
status: done
priority: medium
state_hub_task_id: "7ad8ae85-5ba8-40e3-9c9a-facf918f2b16"
```
On `/exit` or session end:
- Offer inline Profile 1 lesson capture (reuse `memory/reflections.py` helpers)
- Optional `cya retrospect`-style mini-flow without leaving shell history
- `/export-session` — write redacted session summary JSON for knowledge transfer
- Store episodic turns with `kind: session_turn` (new constant) when user confirms
**Acceptance criteria:**
- Session export is user-triggered and inspectable.
- Reflections from shell session appear in `cya memory reflections`.
- Skip path leaves no orphan records.
### T07 — Weakness hints (rule-based v1)
```task
id: CYA-WP-0007-T07
status: done
priority: medium
state_hub_task_id: "fbdd4f04-76ef-4a18-8c40-b41a134a743b"
```
Deterministic post-turn or end-session hints:
- Credential anti-patterns in discussed commands (paste keys, `warden route` bypass)
- Repeated destructive suggestions without confirmation
- State Hub alignment warnings (active work not matching stated goal)
Surface as informational panels — not auto-fixes. Optional save as reflection.
**Acceptance criteria:**
- At least three hint rules with tests.
- Hints never change risk level or skip confirmation.
### T08 — Tests, docs, and operator guide
```task
id: CYA-WP-0007-T08
status: done
priority: high
state_hub_task_id: "3bed6582-a11e-462a-843a-271c842b0103"
```
- `tests/test_shell_session.py` — REPL loop, history opt-in, hub degrade, safety
- README section: starting a shell session, history opt-in, hub commands
- AGENTS.md command reference update
- Example operator session in docs
**Acceptance criteria:**
- `make test` passes.
- README documents `cya shell` with example transcript.
### T09 — Register, sync, and handoff
```task
id: CYA-WP-0007-T09
status: done
priority: low
state_hub_task_id: "3c932e69-ee0a-4135-be11-f890da09509d"
```
Run `make fix-consistency REPO=can-you-assist`, log progress, move to `active` when
implementation begins.
## Success Criteria
When complete:
- Operators can run `cya shell`, pick up recent shell context (opt-in), see hub work,
get multi-turn assistance with full safety, and export learnings for transfer.
- All artifacts remain user-controlled and explainable.
- SCOPE reflects interactive session as an owned capability.
---
**Status note:** Promoted to `ready` on 2026-06-22 after operator approval of direction.
Pair with CYA-WP-0008 for production LLM quality in the REPL.
## Completion Note - 2026-06-23
Implemented in this repo by Codex. Delivered `cya shell` with local JSONL
session artifacts, optional capped/redacted shell history, State Hub orientation
and explicit hub slash-command writes, `/explain`, `/export-session`, end-session
Profile 1 learning hooks, opt-in `session_turn` memory persistence, deterministic
weakness hints, docs, and tests.
Verification:
```bash
make test
python3 -m cya.cli.main --offline hello
python3 -m cya.cli.main shell --help
git diff --check
```
Operator follow-up: after this workplan file change, run from `~/state-hub`:
```bash
make fix-consistency REPO=can-you-assist
```

View File

@@ -0,0 +1,179 @@
---
id: CYA-WP-0008
type: workplan
title: "llm-connect Adapter Integration for Production Assistance"
domain: agents
repo: can-you-assist
status: finished
owner: grok
topic_slug: foerster-capabilities
created: "2026-06-22"
updated: "2026-06-22"
state_hub_workstream_id: "f713db12-5b90-4453-8fbb-a0e50f61699b"
---
# CYA-WP-0008: llm-connect Adapter Integration
## Goal
Replace `FakeLLMAdapter` on production code paths with a real **llm-connect** client
behind the existing `LLMAdapter` Protocol, so one-shot `cya "..."` and `cya shell`
(CYA-WP-0007) can deliver genuinely useful multi-turn assistance.
Preserve the seam: `cya` never hard-codes a vendor; all provider config lives in
llm-connect. Credential routing via `warden route` before requesting secrets.
## Background & References
- **Seam:** `src/cya/llm/adapter.py``LLMAdapter` Protocol + `FakeLLMAdapter`
- **INTENT.md:** `cya` asks; `llm-connect` reaches infrastructure
- **SCOPE.md:** real client listed as explicit out-of-scope / future work
- **CYA-WP-0007:** REPL usable with fake adapter for scaffolding; quality needs this slice
- **Credential routing:** `AGENTS.md` / `warden route find` — no secrets in repo
## Non-Goals
- Provider-specific UI inside `cya` (model picker beyond minimal config)
- Token billing dashboards or cost analytics
- Embedding / RAG pipelines
- Bypassing `LLMAdapter` Protocol for any production path
## Dependencies
- **llm-connect** package/API stability (coordinate with that repo owner)
- Operator-provided credentials via OpenBao paths surfaced by `warden route`
- CYA-WP-0007 T05 can proceed in parallel using `FakeLLMAdapter`
## Task Breakdown
### T01 — Adapter contract review and llm-connect API survey
```task
id: CYA-WP-0008-T01
status: done
priority: high
state_hub_task_id: "483d13bb-aabe-48ad-96c2-8df83de5f442"
```
Document mapping from `AssistanceRequest` / `AssistanceResponse` to llm-connect calls.
Identify config surface (TOML keys, env vars). Note gaps requiring llm-connect changes.
**Acceptance criteria:**
- Short integration note in `docs/` or workplan appendix.
- Credential route catalog id(s) documented via `warden route find`.
**Delivered:** `docs/llm-connect-integration.md`
### T02 — Implement `LLMConnectAdapter`
```task
id: CYA-WP-0008-T02
status: done
priority: high
state_hub_task_id: "0fc17ad5-d90b-4ad1-b060-a1a2f9c25ea8"
```
New class in `src/cya/llm/` implementing `LLMAdapter`:
- Delegates to llm-connect client
- Graceful degrade message when llm-connect unavailable / misconfigured
- `FakeLLMAdapter` remains for tests and `--offline` mode
**Acceptance criteria:**
- Protocol-compliant; swap via config or env (`CYA_LLM_ADAPTER=connect|fake`).
**Delivered:** `src/cya/llm/connect_adapter.py`, `src/cya/llm/factory.py`
### T03 — Configuration and developer ergonomics
```task
id: CYA-WP-0008-T03
status: done
priority: medium
state_hub_task_id: "e8470a37-ecec-42f1-920b-ccd8b98b5512"
```
- `~/.config/cya/config.toml` `[llm]` section (backend, model hints)
- Document `warden route` steps in README
- `make dev-install` optional extra `[llm]` dependency group if needed
**Acceptance criteria:**
- Operator can configure adapter without editing source.
- No secrets committed; example config uses placeholders.
**Delivered:** `src/cya/config.py`, `docs/cya-config.example.toml`, README section
### T04 — Orchestrator and shell integration
```task
id: CYA-WP-0008-T04
status: done
priority: high
state_hub_task_id: "f2781963-fecf-4576-96de-bd745df271a0"
```
Wire `handle_request()` and CYA-WP-0007 shell turns to adapter selection:
- Default fake when unconfigured (current behavior)
- Real adapter when config present
- Multi-turn context: pass recent session turns in `AssistanceRequest.context`
**Acceptance criteria:**
- One-shot and shell paths use same adapter factory.
- Session context bounded (token/line budget documented).
**Delivered:** `get_adapter()` wired in orchestrator; `session_turns` + `bound_session_turns()` ready for CYA-WP-0007 shell
### T05 — Tests and offline CI strategy
```task
id: CYA-WP-0008-T05
status: done
priority: high
state_hub_task_id: "32de980b-1a24-4159-9550-7c516570cae3"
```
- Mock llm-connect for unit tests (no live API in default `make test`)
- Optional integration test marker `@pytest.mark.llm_live` for manual runs
- Fake adapter remains default in CI
**Acceptance criteria:**
- `make test` passes without network or API keys.
- Live test documented for operator manual verification.
**Delivered:** `tests/test_llm_factory.py`, `tests/test_llm_connect_adapter.py`, `tests/test_llm_prompt.py`
### T06 — Documentation and SCOPE update
```task
id: CYA-WP-0008-T06
status: done
priority: medium
state_hub_task_id: "2d152d4b-e4b2-4a94-8f85-d8f033e55d5f"
```
Update README, SCOPE.md (remove "only FakeLLMAdapter" where accurate), AGENTS.md.
**Acceptance criteria:**
- Clear "configured vs offline" operator paths documented.
### T07 — Register, sync, and handoff
```task
id: CYA-WP-0008-T07
status: done
priority: low
state_hub_task_id: "2fb42517-b2df-43d3-8195-f02d310107dc"
```
`make fix-consistency`, progress event, coordinate with llm-connect repo if API gaps found.
## Success Criteria
- Configured `cya` uses llm-connect for real inference.
- Unconfigured / test environments behave exactly as today (fake adapter).
- CYA-WP-0007 shell session becomes practically useful once both workplans complete.
---
**Status note:** `finished` on 2026-06-22. Integration doc: `docs/llm-connect-integration.md`.