generated from coulomb/repo-seed
tegwick
9e6f8a6e08
Three workplans queued and registered with the State Hub (via REST — MCP write layer is erroring this session): - AGENTIC-WP-0007 Phase 3 Distribute: per-flavor distributor adapters render approved catalog patterns into proposed (HITL) artifacts, scoped by repo/domain. - AGENTIC-WP-0008 Read-before-Edit reflex: act on the #1 friction finding. - AGENTIC-WP-0009 Phase 4 Measure: baseline + before/after effectiveness + trend. Proceeding in that order. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
69 lines
2.2 KiB
Markdown
69 lines
2.2 KiB
Markdown
---
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id: AGENTIC-WP-0009
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type: workplan
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title: "Coding Session Memory — Phase 4 (Measure: effectiveness + fleet trend)"
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domain: helix_forge
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repo: agentic-resources
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status: ready
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owner: codex
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topic_slug: helix-forge
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created: "2026-06-07"
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updated: "2026-06-07"
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state_hub_workstream_id: "99f1d836-3be0-40e5-9f17-63d3ecc5fcca"
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---
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# Coding Session Memory — Phase 4 (Measure)
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Implements **Measure** (PRD §6.5, FR-M1–FR-M3) — the loop-closer. After patterns
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are distributed (Phase 3) and changes land (e.g. the State Hub skill
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[STATE-WP-0058] and the Read-before-Edit reflex
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[AGENTIC-WP-0008](AGENTIC-WP-0008-read-before-edit-reflex.md)), Measure answers:
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**did it actually help?**
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Reuses what is already captured — WP-0005 tool buckets, WP-0006 error mining — so
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this is computation over existing digests, not new capture.
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## Baseline Metrics Module + Persisted Baseline
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```task
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id: AGENTIC-WP-0009-T01
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status: todo
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priority: high
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state_hub_task_id: "e5c2016a-2d51-4382-a013-7153e053e8ed"
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```
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`session_memory/measure/metrics.py`: compute fleet metrics over real sessions
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(infra-overhead share, error rate, recurring-error count, schema-thrash, cost
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percentiles) and persist a **timestamped baseline snapshot**. Reuses
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`detect.signals.tool_bucket` and the digest `error_snippets`. Unit-tested.
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## Before/After Per-Pattern Effectiveness
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```task
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id: AGENTIC-WP-0009-T02
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status: todo
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priority: high
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state_hub_task_id: "aa097a00-3462-41da-a137-67e1d61d8d33"
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```
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Given a change/pattern with an applied-at date, compare sessions **after** it
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against the pre-change baseline (cost, error rate, infra-overhead, success) to
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surface **per-pattern effectiveness** so ineffective patterns can be revised or
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retired (FR-M1/FR-M2). Unit-tested.
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## Fleet-Trend Report + Entrypoint + Tests
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```task
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id: AGENTIC-WP-0009-T03
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status: todo
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priority: medium
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state_hub_task_id: "f1147d59-2fb7-4d35-baec-b8f001bb9d62"
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```
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`python -m session_memory.measure`: fleet-level trend (is the median session
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getting cheaper / more reliable over time, FR-M3) plus per-pattern effectiveness;
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markdown + JSON. Document in `session_memory/README.md`. After updates, notify the
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operator to run `make fix-consistency REPO=agentic-resources`.
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[STATE-WP-0058]: handed off to the state-hub repo worker
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