Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mycelium-hq/ai-brain-starter --skill backfill-journal-body-contextgit clone --depth 1 https://github.com/mycelium-hq/ai-brain-starterWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mycelium-hq/ai-brain-starter/backfill-journal-body-context)<a href="https://agentmods.dev/skills/mycelium-hq/ai-brain-starter/backfill-journal-body-context"><img src="https://agentmods.dev/badge/skills/mycelium-hq/ai-brain-starter/backfill-journal-body-context/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mycelium-hq/ai-brain-starter/backfill-journal-body-context"><img src="https://agentmods.dev/badge/skills/mycelium-hq/ai-brain-starter/backfill-journal-body-context.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 15 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00100 | $0.02084 |
| Opus 5 | $0.00050 | $0.01042 |
| Sonnet 5 | $0.00020 | $0.00417 |
| Haiku 4.5 | $0.00010 | $0.00208 |
Grade A, and why
backfill-journal-body-context scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
/weeklyand/monthlywould surface if they had been pulling body data all along - After running
/health-setupfor 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
- Determine the date range. Default:
--year <current-year>(Jan 1 to today). - Find journal entries in that range using
[VAULT_PATH]/⚙️ Meta/journal-index.json(orMeta/journal-index.jsonon non-emoji vaults; rebuild if stale). - 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_sleep_score(date)for the scores - Call
health_lab_panel(date, lookback_days=180)for any out-of-range markers active that period - Pair the body data with the entry's floor tag (from frontmatter
floor_level+floor) - Render the body-track section using the template below
- Append BELOW the original content with a clear divider
- Print summary: N entries processed, M backfilled, K skipped.
Body track template
Append below the original journal content, after a blank line + horizontal rule:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 162 lines · 100 tokens per session scan A f87248379af9
backfill-journal-body-context is a skill published in the GitHub repository mycelium-hq/ai-brain-starter (36 stars, last pushed yesterday), licensed MIT. It adds 100 tokens to every session and 2,084 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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