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Author	SHA1	Message	Date
alvis	1d9a395591	feat(agents): quiet window + peak hours + tz prefs for time-of-day agent (#112 ) Adds four InferredParams (all TTL=24h, min_history=50 except preferred_hour=10): - quiet_start / quiet_end: longest contiguous below-baseline hour run (HH:MM) - peak_hours: top-quartile done-event hours, sorted ascending - tz: cold-start only ("UTC"); populated from auth provider, no inference function compute() updated: - in_quiet check (quiet window) takes precedence over peak hours - in_peak emits "peak productivity hour" language when current hour is in peak_hours - approaching peak (within 2h) surfaces for orchestrator timing - tz surfaced in snippet header when not UTC - snapshot adds peak_hours, in_quiet, in_peak, tz - Agent bumped to v1.2.0 - 21 new tests: night-owl, early-bird, shift-worker, quiet/peak snippet rendering - Fixed test_snapshot_keys in test_agents.py to include new snapshot fields Closes #112 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-06 06:05:51 +00:00
alvis	ad6747c242	feat(profile): /api/profile + eligibility filter + inference framework (ADR-0014 steps 4-6) Step 4 — /api/profile read-through API: GET /api/profile → { user, prefs, consents, contexts } PATCH /api/profile/prefs/:scope upsert user_preferences (source='user') PATCH /api/profile/consents grant / revoke consent keys PATCH /api/profile/contexts create / activate / deactivate contexts Legacy consentGiven bit folded in as data:core fallback. Step 5 — registry-driven eligibility filter: fetchRegistry() exported from agent-registry.ts. profile/eligibility.ts: getEligibleAgentIds(userId) — filters by required consents, silenced_in_contexts, and user_preferences[enabled=false]. fetchOrchestratorTip filters agent_outputs to eligible set before calling ml/serving /recommend. Fail-closed: registry unavailable → empty set. Step 6 — shared context-inference framework (#111) + time-of-day proof (#112): ml/agents/inference/: UserHistory, FeedbackEvent, run_inference(). Framework: cold-start, min_history gating, error fallback, structured logs. TimeOfDayAgent v1.1.0: inferred_params=[preferred_hour]; also reads quiet_start/quiet_end from agent_prefs. agent_prefs injected by TS caller. AgentInput gains agent_prefs field. ml/serving: POST /agents/{agent_id}/infer endpoint. agent-outputs.ts computeAndStore: loads prefs before compute, calls /infer after, persists results (source='inferred'); user overrides never touched. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-05 11:14:25 +00:00

Author

SHA1

Message

Date

alvis

1d9a395591

feat(agents): quiet window + peak hours + tz prefs for time-of-day agent (#112 )

Adds four InferredParams (all TTL=24h, min_history=50 except preferred_hour=10):
- quiet_start / quiet_end: longest contiguous below-baseline hour run (HH:MM)
- peak_hours: top-quartile done-event hours, sorted ascending
- tz: cold-start only ("UTC"); populated from auth provider, no inference function

compute() updated:
- in_quiet check (quiet window) takes precedence over peak hours
- in_peak emits "peak productivity hour" language when current hour is in peak_hours
- approaching peak (within 2h) surfaces for orchestrator timing
- tz surfaced in snippet header when not UTC
- snapshot adds peak_hours, in_quiet, in_peak, tz

- Agent bumped to v1.2.0
- 21 new tests: night-owl, early-bird, shift-worker, quiet/peak snippet rendering
- Fixed test_snapshot_keys in test_agents.py to include new snapshot fields

Closes #112

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-05-06 06:05:51 +00:00

alvis

ad6747c242

feat(profile): /api/profile + eligibility filter + inference framework (ADR-0014 steps 4-6)

Step 4 — /api/profile read-through API:
  GET  /api/profile          → { user, prefs, consents, contexts }
  PATCH /api/profile/prefs/:scope  upsert user_preferences (source='user')
  PATCH /api/profile/consents      grant / revoke consent keys
  PATCH /api/profile/contexts      create / activate / deactivate contexts
  Legacy consentGiven bit folded in as data:core fallback.

Step 5 — registry-driven eligibility filter:
  fetchRegistry() exported from agent-registry.ts.
  profile/eligibility.ts: getEligibleAgentIds(userId) — filters by required
  consents, silenced_in_contexts, and user_preferences[enabled=false].
  fetchOrchestratorTip filters agent_outputs to eligible set before calling
  ml/serving /recommend. Fail-closed: registry unavailable → empty set.

Step 6 — shared context-inference framework (#111) + time-of-day proof (#112):
  ml/agents/inference/: UserHistory, FeedbackEvent, run_inference().
  Framework: cold-start, min_history gating, error fallback, structured logs.
  TimeOfDayAgent v1.1.0: inferred_params=[preferred_hour]; also reads
  quiet_start/quiet_end from agent_prefs. agent_prefs injected by TS caller.
  AgentInput gains agent_prefs field.
  ml/serving: POST /agents/{agent_id}/infer endpoint.
  agent-outputs.ts computeAndStore: loads prefs before compute, calls /infer
  after, persists results (source='inferred'); user overrides never touched.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-05-05 11:14:25 +00:00

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