longitudinal

longitudinal is a skill for Claude Code from mycelium-hq/ai-brain-starter. It costs 114 tokens per session (1,332 once invoked), scanned A, original, MIT.

A health-analysis skill that looks for strong patterns across multiple years of wearable health and journal data. It compares measures such as sleep, heart-rate variability, VO2max, cycles, symptoms, mood, and habits.

In plain words
What is it for?
Use it to investigate health trends over a chosen time window and compare body measurements with journaled moods, symptoms, or activities.
Why use it?
It filters out weak correlations so long-term reports focus on signals that are less likely to be random noise.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the ai-brain-starter plugin — 38 skills, 17 commands, 3 agents, 1 hook shipped together

Good fit Use it to investigate health trends over a chosen time window and compare body measurements with journaled moods, symptoms, or activities.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mycelium-hq/ai-brain-starter/longitudinal
Install

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.

Any agent
npx skills add mycelium-hq/ai-brain-starter --skill longitudinal
Clone the repo
git clone --depth 1 https://github.com/mycelium-hq/ai-brain-starter

Made for: Claude Code.

Or install ai-brain-starter, the plugin that ships this one along with the rest of its 38 skills, 17 commands, 3 agents, 1 hook.

Wrote 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.

agentmods badge for longitudinal

README.md
[![agentmods](https://agentmods.dev/badge/skills/mycelium-hq/ai-brain-starter/longitudinal/github.svg)](https://agentmods.dev/skills/mycelium-hq/ai-brain-starter/longitudinal)
Your own site
<a href="https://agentmods.dev/skills/mycelium-hq/ai-brain-starter/longitudinal"><img src="https://agentmods.dev/badge/skills/mycelium-hq/ai-brain-starter/longitudinal/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.

agentmods 80×15 button for longitudinal

Your own site · 80×15
<a href="https://agentmods.dev/skills/mycelium-hq/ai-brain-starter/longitudinal"><img src="https://agentmods.dev/badge/skills/mycelium-hq/ai-brain-starter/longitudinal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 114 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,332 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00114 $0.01332
Opus 5 $0.00057 $0.00666
Sonnet 5 $0.00023 $0.00266
Haiku 4.5 $0.00011 $0.00133

Measured 11d ago against content hash 9a94c1e81713, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

longitudinal 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 11d 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.

skills/longitudinal/SKILL.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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 today
  • Ny -> 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:

SELECT MIN(start_date) FROM records WHERE value IS NOT NULL

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:

health_floor_body_fingerprint(floor=<name>, vault_root=..., lookback_days=N)

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).

Read the full file on GitHub · 102 lines

Changes

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.

  1. 11d ago First seen · 102 lines · 114 tokens per session scan A 9a94c1e81713

Subscribe to this mod's changes

longitudinal is a skill published in the GitHub repository mycelium-hq/ai-brain-starter (36 stars, last pushed 2d ago), licensed MIT. It adds 114 tokens to every session and 1,332 once invoked, about $0.0006 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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