- integrations.ts: grant data:<provider> on OAuth callback, revoke on disconnect
- Backfill migration: INSERT OR IGNORE data:<provider> for all active tokens
- Agent manifests: drop agent:<id> from required_consents (momentum, time-of-day,
overdue-task, recent-patterns, health-vitals) — per-agent control is a preference
- eligibility.ts: update comment to reflect data:-only consent model
- test_manifest.py: assert no agent: consents remain in any manifest
- migrations.test.ts: backfill idempotency tests for issue #127
- Dockerfile.api: drop --offline flag (fixes ERR_PNPM_NO_OFFLINE_META)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds two InferredParams (TTL=7d) computed from 28-day rolling daily done counts:
- baseline_completions_per_day: mean done events/day over the window
- stdev: stdev of daily counts (floored at 0.1 to avoid division by zero)
MomentumAgent.compute() now calculates a z-score from recent done events in
inp.feedback_history vs the inferred baseline. Snippet language switches to
z-score framing ("above your usual pace", "slowing down") when |z| >= 1.0,
falling back to engagement_trend labels when in the normal range.
- engagement_trend InferredParam preserved for backward compatibility
- momentum_window pref added (default 7, user-overridable)
- 14 new tests covering power user, casual user, returning-from-break, and
relative stdev comparison; engagement_trend tests updated for z-score priority
- Agent bumped to v1.2.0
Closes#114
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
All four agents bumped to v1.1.0.
momentum (#114): infers engagement_trend ('up'|'stable'|'down') by comparing
done-rate in the last 7 days vs the prior 7 days. Agent surfaces the trend
in its snippet ("trending up — build on the momentum").
overdue-task (#115): infers lateness_tolerance_days (0/1/2) from snooze rate.
Agent now filters tasks against the tolerance so low-urgency users aren't
nagged about tasks that are only hours overdue.
recent-patterns (#116): infers window_days (7/14/30) from feedback event
density — sparse users get a wider window so the snippet isn't always empty.
focus-area (#113): no inferred params (project-level feedback linkage needed,
tracked under #78). preferred_areas pref was declared but ignored; agent now
honours it as a tiebreaker and mentions it in the snippet.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Each agent now exports a module-level MANIFEST declaring id, version,
pref_schema, required_consents, ttl_sec, and silenced_in_contexts. The
registry surfaces both the agent and its manifest, and rejects on
mismatch so the two cannot drift.
ml/serving exposes GET /agents/registry; services/api proxies it as
GET /api/agents/registry with a 60s in-process cache so admin pageviews
don't hammer upstream. Failures aren't cached.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds ml/agents/ — five specialised sub-agents (overdue_task, momentum,
time_of_day, recent_patterns, focus_area) each producing a prompt snippet
from user signals. A registry wires them up; the orchestrator prompt in
ml/serving/prompts.py synthesises their outputs into one tip via LiteLLM.
Also wires /api/agents route in the API and updates the Dockerfile to copy
the full ml/ tree with PYTHONPATH=/app so agent imports resolve correctly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
