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docs: update focus-area manifest description and CLAUDE.md

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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alvis
2026-05-12 15:00:06 +00:00
parent f66f337779
commit 34925310cf
2 changed files with 5 additions and 5 deletions
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8
CLAUDE.md
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@@ -92,7 +92,7 @@ oO generates tips through a multi-agent pipeline (ADR-0013): pre-compute agents
| Alias | Model | Used by |
|-------|-------|---------|
| `tip-generator` | qwen2.5:1.5b (default) | `ml/serving` tip generation |
| `embedder` | nomic-embed-text | task clustering, dedup |
| `embedder` | nomic-embed-text | task clustering (after LLM enrichment), dedup |
| `judge` | claude-haiku-4-5 (cloud, eval only) | offline sim |
Env vars: `LITELLM_URL` (prod `https://llm.alogins.net`), `OLLAMA_URL` (Agap host, `http://host.docker.internal:11434` from containers).
@@ -197,7 +197,7 @@ Recent completions:
- ADR-0012 — ε-greedy v2 (D=12) — 2026-04-26 (now superseded by ADR-0013)
- ADR-0014 complete: unified Profile schema + backfill, manifest plumbing, `/api/profile` read-through, registry-driven eligibility filter, inference framework + per-agent inference, legacy consent column drop — 2026-05-05
- Rich per-agent inference for all four active agents (#112, #114, #115, #116) — 2026-05-06: quiet/peak hours (time-of-day), z-score baseline (momentum), p50 lateness + project realness (overdue-task), adaptive lookback + weekly/daily cycles (recent-patterns)
- Semantic task clustering via nomic-embed-text + focus-area preferred_areas inference (#97, #113) — 2026-05-06: `ml/agents/clustering.py`, focus-area v2.0.0
- Semantic task clustering via nomic-embed-text + LLM enrichment (#97, #113, #129) — 2026-05-12: `ml/agents/clustering.py`; titles expanded via `tip-generator` before embedding; persistent cache in `task_enrichments` table; recompute gated on task-list hash change; focus-area v3.0.0 outputs all clusters with enriched descriptions
- Per-user feature freshness SLAs (#61) — 2026-05-06: `invalidated_by` mirrored into `ProfileFeature`; drift-detection test added
- MLflow tracing added to `ml/serving` for all agent calls — 2026-05-06: `ml/serving/mlflow_client.py`; activated by `MLFLOW_TRACKING_URI=http://mlflow:5000` (default in compose `full` profile); requires `--profile mlops` for the MLflow container. Issue #118 (M4) tracks removal from production critical path.
@@ -223,7 +223,7 @@ Lives in `ml/agents/inference/`. `run_inference(manifest, history)` evaluates al
- `infer()` error → emit `cold_start_default` (never crashes)
- Results written to `user_preferences` with `source='inferred'`; keys with `source='user'` are never overwritten
All five agents are at v1.2.0. Per-agent inferred params (all live in `ml/agents/<name>.py`):
Per-agent inferred params (all live in `ml/agents/<name>.py`):
| Agent | Inferred params | Notes |
|-------|----------------|-------|
@@ -231,7 +231,7 @@ All five agents are at v1.2.0. Per-agent inferred params (all live in `ml/agents
| `momentum` | `engagement_trend`, `baseline_completions_per_day`, `stdev` | Baseline = 28d rolling mean done/day; snippet uses z-score language |
| `overdue-task` | `lateness_tolerance_days`, `project_realness` | Tolerance = p50 lateness from TaskCompletion history; realness = project median vs global median |
| `recent-patterns` | `lookback_days`, `weekly_cycle`, `daily_cycle` | Lookback sized to ≥30 done events; cycles use peak-to-mean ratio; snippet hints when strength > 0.5 |
| `focus-area` | `preferred_areas` | Top-2 project IDs by task completion count; semantic clustering via `ml/agents/clustering.py` in compute() |
| `focus-area` | *(none)* | No inferred params. Clusters tasks via LLM-enriched embeddings and outputs all areas with expanded descriptions. Recomputes only when task list changes (hash-gated). |
`UserHistory` carries both `events: list[FeedbackEvent]` and `task_completions: list[TaskCompletion]`. `AgentInferRequest` (ml/serving) accepts `task_completions: list[dict]` alongside `feedback_history`.

2
ml/agents/focus_area.py
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@@ -10,7 +10,7 @@ from .manifest import AgentManifest
MANIFEST = AgentManifest(
id="focus-area",
version="3.0.0", # output all clusters as context; no scoring (#129)
description="Clusters the user's task list and summarises all areas for the orchestrator.",
description="Clusters tasks semantically, enriches titles via LLM, and outputs a full area summary with expanded descriptions for the orchestrator.",
pref_schema={"type": "object", "additionalProperties": False, "properties": {}},
context_schema=["todoist.tasks"],
required_consents=["data:core", "data:todoist"],