alvis bc71dc203d feat(agents): adaptive lookback + weekly/daily cycle detection for recent-patterns (#116) ...

Replaces the coarse density-bucket window_days with three InferredParams (all TTL=24h):
- lookback_days: min window containing ≥30 done events, capped at 30d (min_history=5)
- weekly_cycle: per-DOW peak-to-mean strength list (min_history=21, ≥3 weeks of signal)
- daily_cycle: per-hour peak-to-mean strength list (min_history=14)

compute() renders cycle hints when strength > 0.5:
  "User tends to complete tips on Tuesdays and Saturdays."
  "User is most active around 8pm."
Legacy window_days pref key still accepted as a fallback.

- window_days pref renamed lookback_days; backward-compat fallback in compute()
- Agent bumped to v1.2.0
- 19 new tests: weekend-warrior, weekday-only, evening-person, no-pattern,
  legacy compat, snippet rendering with strong/weak signals

Closes #116

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

2026-05-06 05:51:45 +00:00

..

__init__.py

feat(ml): multi-agent context framework + v4 orchestrator prompt

2026-05-04 10:20:05 +00:00

test_agents.py

feat(agents): per-agent inference — momentum, overdue-task, recent-patterns, focus-area (ADR-0014 step 7)

2026-05-05 11:21:10 +00:00

test_inference.py

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

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

test_manifest.py

feat(agents): manifest plumbing + GET /agents/registry (ADR-0014 step 3)

2026-05-05 10:55:54 +00:00

test_per_agent_inference.py

feat(agents): adaptive lookback + weekly/daily cycle detection for recent-patterns (#116)

2026-05-06 05:51:45 +00:00