generated from coulomb/repo-seed
docs: update SCOPE.md post-0004 + new gap analysis (2026-05-28) vs INTENT.md
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SCOPE.md
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SCOPE.md
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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.
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## Current Status (Post CYA-WP-0002 Memory Slice)
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## Current Status (Post CYA-WP-0004 Packaging & Distribution Slice)
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Two implementation slices have been delivered:
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Four implementation slices have been delivered:
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- **CYA-WP-0001 (Console-Native MVP)**: Core CLI, context collection, rule-based safety with mandatory confirmation, LLMAdapter seam, and thin memory ports.
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- **CYA-WP-0002 (Memory Integration)**: Real user-controlled, persisting memory implementation behind the explicit ports, wired into the orchestrator and `--explain-context`, memory signals integrated into safety, dedicated tests, and documentation.
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- **CYA-WP-0001 (Console-Native MVP)**: Core CLI, bounded context collection, rule-based safety with mandatory confirmation, LLMAdapter Protocol seam, basic orchestrator.
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- **CYA-WP-0002 (Memory Integration)**: Real user-controlled, persisting memory (scoped JSON) behind explicit ports, wired into context and safety.
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- **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).
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- **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.
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Core capabilities now include:
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- Natural language request handling via a clean Typer CLI.
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- Bounded, transparent, non-recursive local context collection.
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- Genuine rule-based risk classification (now memory-aware) with mandatory terminal confirmation.
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- Stable `LLMAdapter` Protocol boundary.
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- Real, user-controlled memory for preferences and workflow patterns (scoped JSON backing under `~/.config/cya/memory/`, fully inspectable and user-editable).
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- Memory surfaced in explanations and fed into safety decisions.
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- A small orchestrator coordinating the pipeline.
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- Natural language request handling via clean Typer CLI.
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- Bounded, transparent local context collection.
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- Genuine rule-based (memory-aware) risk classification with mandatory confirmation.
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- Stable `LLMAdapter` Protocol.
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- Real, user-controlled, contextually activated memory (directory/project scoped) with retrospection support.
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- Automatic memory activation based on working directory/git root.
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- `cya retrospect` for structured reflection and goal setting.
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- Full developer workflow: dev-head install, testing, building distribution packages, and a documented release process.
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- Transparent, inspectable behavior via `--explain-context`.
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All LLM interaction flows through the documented adapter seam. Memory flows through explicit ports. No production path bypasses these boundaries.
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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.
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## Owns
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- Autonomous or background execution of commands without explicit user confirmation.
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- Deep repository indexing, embeddings, or large-scale content analysis (explicit non-goal of the first slice).
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- Voice, speech, phone-bridge, or non-terminal interfaces (future work).
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- Packaging, distribution, or multi-platform installers beyond basic editable install.
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- Production PyPI publishing and automated release CI (documented process and local tooling exist; actual publication is future work).
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- Long-lived conversational REPL or session state (one-shot + very lightweight session only).
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## Integrates With
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| Project | Responsibility | Integration Style |
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|---------------|-----------------------------------------|------------------------------------|
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| `llm-connect` | Provider access, config, token counting, structured responses | Stable `LLMAdapter` Protocol |
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| `phase-memory`| User-controlled memory, preferences, history | Explicit ports with real (local JSON) implementation (T02); long-term target is deeper profile-driven integration |
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| `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 |
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| State Hub | Work tracking, decisions, coordination | HTTP REST (non-runtime) |
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## MVP Scope (CYA-WP-0001)
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## Current Delivered Scope (Post 0004)
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What was explicitly in scope for the first slice:
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Significant slices have been delivered beyond the original MVP:
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- A functional `cya` CLI that accepts natural language requests.
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- Safe, bounded context collection with full transparency (`--explain-context`).
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- Rule-based safety classification as the primary mechanism, with mandatory confirmation.
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- A clean, documented adapter boundary for future real LLM backends.
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- Real user-controlled memory implementation (scoped, persisting, explainable) behind the explicit ports, with a clean seam for future deeper `phase-memory` integration.
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- Basic orchestrator that ties the pieces together.
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- Test coverage focused on safety invariants and context rules.
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- Clear public boundaries and extension points.
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- Full console-native CLI with rich output.
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- Context-aware, directory/project-bound memory activation.
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- User-driven retrospection and continuous optimization loops (`cya retrospect`).
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- Real, inspectable, user-controlled memory with strong explainability and safety integration.
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- Complete developer workflow: installation from dev head, testing, building distribution packages, and a documented release process.
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- Packaging and distribution now treated as first-class concerns (with registered future work).
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See the individual workplans for detailed scope per slice.
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## Explicitly Out of Scope (Current and Near-Term)
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- Full deep integration with the complete `phase-memory` profile/planner/graph system (current implementation uses a deliberate, user-visible local JSON store as the first real backing; deeper integration is planned future work).
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- Real `llm-connect` client (only the contract + fake exists).
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- Deep git/repository understanding beyond basic status + log + user-declared memory.
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- Automatic command execution (even "safe" suggestions).
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- Structured editing / patch generation.
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- Rich multi-turn conversational state (one-shot + lightweight scoped memory only).
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- 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).
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- Real `llm-connect` client implementation (only the stable `LLMAdapter` Protocol contract + FakeLLMAdapter exists).
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- Deep semantic repository understanding or large-scale content analysis.
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- Automatic command execution (even "safe" suggestions) — explicit user confirmation remains mandatory for anything non-safe.
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- Rich multi-turn conversational state beyond lightweight scoped memory + retrospection.
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- Cost tracking, token budgeting, or usage dashboards.
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- Team/shared memory or collaboration features.
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- Plugin system or domain-specific extensions.
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- Production PyPI publishing and fully automated release CI (a lightweight documented local process exists; automation is future work).
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## Extension Points (Registered)
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- `cya/llm/adapter.py` — `LLMAdapter` Protocol (the primary seam).
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- `cya/memory/__init__.py` — the four explicit ports with real (persisting, user-controlled) implementation behind them.
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- `cya/safety/risk.py` — the `_RULES` table and `classify()` function.
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- `cya/memory/__init__.py` — the explicit ports (with real implementation, contextual activation, and retrospection support).
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- `cya/safety/risk.py` — the `_RULES` table and `classify()` function (memory-aware).
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- `cya/context/collector.py` — collection functions and ignore policy.
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- `cya/orchestrator.py` — the main coordination entry point.
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- Packaging & distribution: `Makefile`, `pyproject.toml`, `docs/release-process.md`, and `MANIFEST.in` (first-class concern with registered future work).
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## Success Criteria (Current Slice)
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## Success Criteria (Current State)
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A new user can:
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- Clone the repo, run `pip install -e .`, and successfully use `cya` for 2–3 realistic tasks after reading only the README.
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- Understand exactly what context is being sent via `--explain-context`.
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A new user or contributor can:
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- Install the latest development code easily (`make dev-install` or direct git+) and use `cya` for realistic tasks after reading the README.
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- Understand exactly what context and memory influenced a response via `--explain-context`.
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- Trust that dangerous actions will never execute without explicit confirmation.
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- See a clear path for how real `llm-connect` and `phase-memory` will plug in later.
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- Use `cya retrospect` to reflect on usage and set goals that influence future behavior.
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- Build and verify distribution packages locally.
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- See a clear path for how real `llm-connect`, deeper `phase-memory`, and future PyPI releases will integrate.
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Sibling project owners can read the workplan + boundary documentation and know precisely where their packages integrate.
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Sibling project owners (llm-connect, phase-memory, State Hub) can read the workplans + boundary documentation and know precisely where their packages integrate.
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---
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**This SCOPE document reflects the state after CYA-WP-0001 (MVP) + CYA-WP-0002 (Memory Integration).**
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**This SCOPE document reflects the state after CYA-WP-0004 (Dev-Head Install & Release Packaging).**
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It is intentionally narrower than the long-term vision in INTENT.md and MemoryVision.md, but now includes a real first slice of user-controlled memory as delivered by 0002.
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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.
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See `workplans/CYA-WP-0002-memory-integration-roadmap.md` and `MemoryVision.md` for the intended direction of deeper `phase-memory` integration.
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See the individual workplans (especially CYA-WP-0003 and CYA-WP-0004) and `MemoryVision.md` for the intended direction of deeper `phase-memory` integration and future evolution.
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