Computes natal chart (Sun/Moon/Mercury/Venus/Mars/Jupiter/Saturn) from
birth_date and finds active transits (conjunction/sextile/square/trine/
opposition) between today's sky and the user's natal positions. Top 3
most-exact transits are passed to the orchestrator as interpretive themes
to colour the tip — grounded and actionable, not predictive.
Birth date sourced from agent_prefs (populated by a connected Google
data source); requires data:google-health consent. Agent self-silences
when birth_date is absent. pyswisseph added to ml/serving/requirements.txt.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Draws 3 Major Arcana cards from a daily seed (user_id + date) so the
reading is stable within a day and unique per user. Card meanings and
action hints are precomputed in the agent; the orchestrator receives a
structured prompt snippet and is instructed to weave the themes into a
grounded, practical tip without explaining the cards.
No inferred params, no external data — requires only data:core consent.
TTL 6 h (refreshes at most twice daily).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
OAuth2 flow with all 11 Google Fitness scopes (activity, body, sleep,
heart rate, nutrition, location, blood glucose/pressure/temperature,
oxygen saturation, reproductive health). Stores access + refresh tokens;
auto-refreshes on expiry.
GoogleHealthSignalSource fetches steps, sleep sessions, active minutes,
calories, and heart rate from the Fit aggregate + sessions APIs. Signals
flow into both the tip orchestrator and the health-vitals pre-compute
agent, which generates prompt snippets about step progress, sleep
deficit, sedentary time, and elevated heart rate.
Signal.kind extended with 'health'; IntegrationProvider extended with
'google-health'. Agent compute signal mapping enriched to include source,
kind, and all features so health-vitals can filter its own signals.
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>
