mycelium-hq/ai-brain-starter/longitudinal
Claude Code and agent skill by mycelium-hq/ai-brain-starter
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Checking that the SKILL.md file is still in the repository.
Use when the user wants multi-year health patterns from HealthKit + journal data: "patterns in my health data," "what does my body tell me," "correlations between mood and HRV," "Floor x body fingerprint," long-term trends in sleep, HRV, VO2max, cycle, or symptoms, "how has my body changed over the years," or /longitudinal. NOT for single-day analysis (use /health-doctor), this-week patterns (use /weekly), or journal-only pattern extraction (use /patterns).
Install
npx vskill@latest install mycelium-hq/ai-brain-starter/longitudinalSKILL.md
When the user types /longitudinal, run the multi-year correlation pass and surface only the strongest signals across years of health-mcp + journal data.
Language
Generate the report in the language the user writes in. If Spanish, all sections including the panel commentary are in Spanish.
Scope resolution
Parse the argument for window:
all-> earliest record in DB to todayNy-> last N years (e.g. 5y, 3y)Nm-> last N months (e.g. 18m)- blank -> 365 days
If all, query the DB for the earliest record date first:
Step 1: top_signals first (the noise filter)
Always call health_top_signals(vault_root=..., lookback_days=N, min_strength="moderate") first. This is Lara Briden's dissent codified: most correlations are noise. The substrate has already filtered. Start with what's left.
If signal_count == 0, report "no signals above noise threshold for this window" and stop — do not invent. Surface what IS there: the deltas and r-values that didn't quite clear the threshold, in case the user wants to relax it.
Step 2: Floor x body fingerprints for the user's top 3 Floors
Load the journal index, count Floors in the window, take the top 3 by occurrence.
For each Floor, call:
Report the body fingerprint deltas (HRV, RHR, sleep efficiency, cycle phase distribution). If delta_pct exceeds ±10% AND n_on_floor >= 10, this is a real fingerprint. Below that, mention it but flag as "weak."
Step 3: Sleep architecture trend
Call health_sleep_architecture(start, end) for the window AND for the prior matching window (e.g. 1y now vs 1y prior). Compare REM%, Deep%, Core%, efficiency. Flag drift > 5 percentage points.
Step 4: Longitudinal markers
Call health_longitudinal_summary(start, end, granularity="quarter"). Pull HRV baseline, VO2max, lean body mass, walking steadiness, sleep efficiency by quarter. Compute trend slope per marker (rough linear regression: (last - first) / first * 100).
Surface only markers with > 5% drift across the window.
Step 5: Symptom correlate (if symptom data present)
Call health_symptom_correlate(symptom_type=None, vault_root=..., lookback_days=N). If any symptom has > 15% delta_pct on a body metric AND n >= 5 days with the symptom, surface it.
Step 6: Named loops (optional, if the user has /patterns output recently)
If [VAULT_PATH]/Meta/Patterns/loops-detected.json exists (created by /patterns), read it. For each named loop with date list, call health_loop_signature(loop_dates_iso=[...], vault_root=...). Surface the loop body fingerprint.
If the file doesn't exist, skip this step silently — no error.
Report format
Critical: zero fabrication
Every number in the report comes from a health-mcp tool call. If a tool returns error, surface the error verbatim and skip that section. Never invent a metric. Never round so heavily that a 38.2 ms HRV becomes "around 40."
If the lookback window includes a period before the user started recording (e.g. they ran /longitudinal all but the DB only has 3 years), report the actual span you found: "earliest record: 2023-04-12, so the window is 2023-04-12 to 2026-05-10 (3.1 years)."
When to escalate
If a single marker shows > 25% drift in the wrong direction (HRV baseline dropping > 25%, VO2max dropping > 25%, lean mass dropping > 10%), surface it AT THE TOP of the report with the marker name in bold and a one-line note. These are health-significant changes worth flagging directly. Do not bury them in section 4.
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/longitudinal/SKILL.md
Popularity
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