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backfill-journal-body-contextlisted

Walks every daily journal entry in a date range (default this year) and appends a "Body track" section BELOW the original verbatim content. Pulls health-mcp data for each date (HRV, RHR, sleep, cycle phase, lab status, recovery/sleep/strain scores) and weaves a Floor-paired interpretation. Idempotent (skips entries that already have the section). Use when user says /backfill-journal-body-context, asks to enrich journals with body data, says "backfill my journals with health" or wants existing journal entries paired with their Apple Health / Oura / Fitbit data retroactively.
adelaidasofia/ai-brain-starter · ★ 18 · AI & Automation · score 78
Install: claude install-skill adelaidasofia/ai-brain-starter
# backfill-journal-body-context Reads existing daily journals, pulls health-mcp data for each entry's date, and appends a "Body track" section BELOW the original verbatim content. The original entry text is NEVER modified — the rule from `feedback_journal_verbatim_words.md` is non-negotiable. ## When to use - User says `/backfill-journal-body-context` - User wants their existing journals enriched with body data (HRV, sleep, recovery, cycle phase) retroactively - User wants to see what `/weekly` and `/monthly` would surface if they had been pulling body data all along - After running `/health-setup` for the first time and importing a backfill window of biometric data Do NOT use for: - Brand-new journal entries (daily-journal already pulls body context via health-context skill) - Editing journal content beyond appending the body section - Anything that touches the original verbatim journal body ## How it works 1. Determine the date range. Default: `--year <current-year>` (Jan 1 to today). 2. Find journal entries in that range using `[VAULT_PATH]/Meta/journal-index.json` (rebuild if stale). 3. For each entry: - Read the file - Check if it already has a `## Body track (health-mcp, backfilled YYYY-MM-DD)` section — if yes, skip (idempotent) - Call `health_journal_context(date, voice_profile="warm")` for the data + rendered prose - Call `health_cycle_context(date)` if cycle data exists (for women's cycle awareness) - Call `health_recovery_score(date)` + `health