Checkpoints end with an explicit verdict (GO, DRIFTED, ON-TRACK) and the reasoning attached. If data is missing or incomplete, it states UNVERIFIABLE. It never guesses or asks open-ended questions.
Checkpoints report only changes against the morning plan. Settled facts are not repeated. If nothing material has changed, the agent outputs "No delta — plan holds" or responds with [SILENT] to suppress notification noise.
Evaluates biometrics and habit consistency against multi-day rolling bands (sleep duration target envelopes, baseline RHR ranges, rolling HRV baselines) rather than overreacting to single-day variance.
The agent presents decisions with all underlying evidence visible. The human retains final override authority.
Audits data freshness before making claims. For sleep data: checks Tier 1 (Finalized database event), falls back to Tier 2 (Raw active night Redis cache), and only marks Tier 3 (Pending sync) when no live records exist.
Executes via the daily-ai-brief MCP and Apple Podcasts store (MTLibrary.sqlite). It ingests newly dropped AI episodes, scans 22 technical engineering feeds, extracts arXiv breakthroughs and frontier lab model drops, and queues actionable takeaways for the morning brief.
Audits sleep data freshness → Evaluates 7-day recovery (HRV baselines, resting HR, workload ratio) → Checks yesterday's macro totals from PostgreSQL → Cross-checks calendar meetings and defends my 09:00–12:00 deep focus block → Outputs the day plan ticket to Linear and Slack.
Audits morning task progress against the plan, checks logged meals to date for protein pacing, triggers a quick camera alertness scan, and emits delta updates to the active day ticket.
Before I step out for a run or workout, it runs a 4-point gate to issue a binary GO / NO-GO verdict:
Reconciles workout data from wearable HR streams, calculates the remaining protein/macro target for dinner, and triggers local Tuya commands to start circadian lighting wind-down.
Closes daily records (training mileage, strength habit checks, nutrition balance, completed Linear tickets), updates rolling composite scores, and flags bedtime drift.
[SILENT] when healthy.
Instead of following a rigid static calendar, workouts are dynamically cleared 30 minutes before departure. The thermostat-coach skill executes a 4-point gate:
Compares last night's HRV peak against the 7-day rolling envelope. If HRV dropped > 15% and resting HR nadir occurred late (> 04:30), intensity is automatically downgraded from threshold intervals to Zone 2 base.
Polls the 17:00–19:00 feels-like temperature window, solar UV index, and AQI. If outdoor air quality exceeds threshold, it routes the workout indoors.
Cross-checks EventKit calendar via macos-mcp. Guarantees a minimum 90-minute block free of calls and travel transitions.
Audits lunch protein and carb intake from Open Wearables to ensure adequate glycogen availability before high-exertion sessions.
reconstruct-run-from-hr
When a workout finishes, the agent reads continuous second-by-second heart rate streams from the Open Wearables database:
run-feedback-analysis · biome.db
Biometrics only tell half the story. Whenever I drop a short note into Slack (e.g. "Ran 8.4km in 46m, felt smooth on climbs, slight right calf tightness, RPE 6"), the run-feedback-analysis skill parses it:
Active workout calories are factored into Total Daily Energy Expenditure (TDEE). The agent calculates the remaining protein and carbohydrate requirements for the day, outputting the exact dinner target in Slack before closing the training ticket on Linear.
food-log Skill)When I drop a meal photo or food description into Slack, the agent processes it:
sips and calls Google AI Studio's vision model to extract plate items and estimate quantities.Image Contents ∪ User Text).Macro numbers feed directly into the daily operational loops:
Nutrition data is persisted across two dedicated local database stores:
meal_log & data_point_series)Maintained in Open Wearables. Stores structured plate items, meal type (breakfast, lunch, dinner, snack), exact ingestion timestamp, and computed macronutrient breakdowns: total calories, protein, carbohydrates, fats, and dietary fiber.
meal_feedback)Captures post-meal qualitative signals: satisfaction rating (1–5), subjective satiety (light, full, stuffed), and subsequent energy levels (energetic vs sluggish) to calibrate future portion estimation models.
Continuous execution tracking across 5 daily beats and 4 operational domains.
48.2 km weekly running volume · 3/3 Strength sessions · 0 pre-exertion gate violations.
7h 48m avg sleep duration · RHR nadir 47 bpm at 03:45 · HRV +8% vs 30-day baseline.
165g daily protein floor maintained · 100% meal photos logged · 0 late-night deficit spikes.
09:00–12:00 calendar blocks defended · 0 meeting collisions · Daily Linear issues closed.
Runs via a deterministic capture script: captures frame from camera → verifies file size (≥15KB) and JPEG header (ffd8ff) → generates SHA-256 capture receipt → extracts fatigue indicators (dark circles, hydration, alertness) → archives to disk with a 30-day retention purge.
daily-ai-brief MCP.
40 tools that give AI agents hands on your Mac: reads and schedules Calendar events (via EventKit), manages Apple Reminders and Apple Notes, controls Apple Music playback, captures FaceTime camera snapshots, checks Mail, and monitors Mac health (CPU load, memory pressure, battery, and thermal state) over local stdio with zero cloud latency.
Pulls overnight intelligence across frontier lab releases, newly dropped AI podcast episodes, open-source model drops, and breakthrough arXiv papers into structured briefing payloads for the 07:30 learning beat.
Searches PubMed, arXiv, and Crossref to ground physiology, recovery, and training queries in peer-reviewed scientific literature rather than training-set approximations.
linear-mcp)Reads active monthly plan tickets, creates daily sub-issues, posts continuity comments, and closes tickets with root-cause notes.
Direct local socket communication to Tuya smart bulbs, IR blasters, and Philips Hue bridges. Executes circadian color temperature curves with zero cloud latency.
Fast programmatic checks validate outputs before notifications are dispatched or tickets are updated:
UNVERIFIABLE.ffd8ff) to prevent blank frame hallucination.GO, DRIFTED, ON-TRACK, UNVERIFIABLE) and refused open-ended questions.[SILENT] or "No delta — plan holds".All agent runs, tool calls, and Slack conversational traces stream directly into Langfuse. During the Monday 09:30 Retrospective, an automated judge model scores decision traces across four rubrics:
Verifies that every cited number (HRV, RHR nadir, protein grams, temperature) maps exactly to the frozen current_context.json snapshot.
Penalizes motivational lecturing, unsolicited life advice, or fluff. Enforces a crisp, objective builder voice.
Audits whether the 4-input pre-exertion gate correctly evaluated calendar, HRV baselines, climate AQI, and fuel floors before issuing a GO verdict.
Confirms that all executed tool actions (Linear updates, Tuya light adjustments) stayed strictly within reversible bounds without destructive side effects.
Real-time telemetry and weekly judge evaluations across 1,400+ trace runs.
telemetry_observations)telemetry.db
What it captures: Continuous time-series readings from room IoT boards (indoor temperature, relative humidity, VOC air quality, ambient lux, acoustic noise in dB, barometric pressure) paired with outdoor climate readings (ambient temp, feels-like heat index, UV index, wind velocity, multi-pollutant AQI, and 3-hour precipitation probability).
Why it stores it: Enables 60-minute rolling rate-of-change velocity calculations to catch sudden heat spikes or deteriorating indoor air quality before focus sessions or workouts begin.
agent_decisions)ledger.db
What it captures: A complete immutable audit log for every scheduled beat (08:00 morning brief, 16:30 exertion gate, 22:30 ledger close). Each record stores the full frozen JSON snapshot of biometrics, schedule, and environment at that second, alongside the agent's synthesized step-by-step reasoning chain, the final verdict (GO, DRIFTED, ON-TRACK, UNVERIFIABLE), and emitted actuator payloads.
Why it stores it: Powers the Monday morning retrospective, enabling automated LLM-as-a-judge evals to inspect why a specific recommendation was made without relying on vague memory.
human_feedback & meal_feedback)biome.db
What it captures: Direct human notes (e.g. typed meal corrections, manual nap notes, workout logs) alongside subjective ratings (1–5 scale), Rate of Perceived Exertion (RPE 1–10), and post-meal energy states (energetic vs sluggish).
Why it stores it: Serves as absolute ground truth. Whenever automated sensor models or vision estimations conflict with direct human entries, the human feedback record takes priority.
composite_scores)ledger.db
What it captures: Daily rolled-up performance metrics spanning workout volume vs weekly plan, strength habit adherence, sleep duration compliance against baseline envelopes, nutrition balance, and deep focus blocks defended.
Why it stores it: Computes 7-day, 14-day, 30-day, and 180-day rolling trend lines so the agent evaluates consistency against long-term envelopes rather than overreacting to single-day variance.
What it captures: Open Wearables' PostgreSQL tables (meal_log and data_point_series for sleep stages, resting HR nadir, continuous HRV, and exercise heart rate) and macOS native Apple Podcasts database (MTLibrary.sqlite for newly downloaded episodes and played timestamps).
Why it stores it: Provides raw biometric and audio intelligence pipelines that feed the 15-minute context platter aggregator.