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 SkillMedev/health-and-longevity --skill longevity-protocolgit clone --depth 1 https://github.com/SkillMedev/health-and-longevityWrote 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/skillmedev/health-and-longevity/longevity-protocol)<a href="https://agentmods.dev/skills/skillmedev/health-and-longevity/longevity-protocol"><img src="https://agentmods.dev/badge/skills/skillmedev/health-and-longevity/longevity-protocol.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00157 | $0.01897 |
| Opus 5 | $0.00078 | $0.00949 |
| Sonnet 5 | $0.00031 | $0.00379 |
| Haiku 4.5 | $0.00016 | $0.00190 |
Grade A, and why
Longevity Protocol 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 8d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Longevity Protocol
The costly mistake in longevity is inversion: obsessing over supplements and biohacks (weak evidence) while sleep, aerobic capacity, strength, and diet (overwhelming evidence) go under-managed. This skill assembles a weekly routine strictly in order of evidence strength and labels every recommendation with its tier, so effort lands where the healthspan return actually is. Healthspan - years in good physical and cognitive function - is the target, not lifespan alone.
Evidence tiers - apply to everything
- Tier 1 (act on these): sleep, exercise (aerobic + strength), dietary pattern, not smoking, social connection. Large, consistent human outcome data.
- Tier 2 (reasonable, weaker data): time-restricted eating, sauna, specific dietary tweaks beyond the core pattern. Plausible mechanisms, mixed or smaller human trials.
- Tier 3 (honest label: mostly unproven for longevity): nearly all supplements marketed for longevity - NAD precursors, resveratrol, and similar. Mechanistic or animal data, little or no human outcome evidence. Never present a Tier 3 item as if it were Tier 1, and never let it displace Tier 1 effort or budget.
Operating procedure
Order matters: pillars are addressed in descending evidence-and-leverage order, and the weekly template is assembled only after every pillar has a concrete dose.
Step 1: Gather inputs
Collect: age and sex; current exercise (type, days, duration); typical sleep hours and consistency; rough dietary pattern; smoking and alcohol; known conditions and medications (conditions mean physician sign-off before the exercise pillars - see Escalation); realistic weekly hours available (default plan needs ~6-7); and what they currently spend on supplements (this often funds the honesty conversation). Label self-reported estimates as estimates.
Step 2: Sleep - the non-negotiable foundation
Target 7-9 hours nightly, with consistency prioritized over duration: a fixed wake time anchors circadian rhythm better than managing bedtime. Core behaviors: no bright screens 30-60 minutes before bed, bedroom at 18-20 °C (65-68 °F), caffeine cut off after 1-2 PM, alcohol limited - even moderate amounts fragment deep sleep. Chronic sleep below 6 hours is associated with accelerated biological aging. For persistent problems, hand off to sleep-optimizer.
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.
- 8d ago First seen · 91 lines · 157 tokens per session scan A 29d2f3de9295
Longevity Protocol is a skill published in the GitHub repository SkillMedev/health-and-longevity (1 stars, last pushed 2mo ago), licensed MIT. It adds 157 tokens to every session and 1,897 once invoked, about $0.0008 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.
Other skills, from other repositories
apple-health
Unofficial Apple Health export MCP for AI agents. Prefer MCP tools if connected; otherwise the package CLI. Use when the user wants Apple Health data or actions through an agent.
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons. Invoke for any question about IDC collections, cancer imaging datasets, DICOM data access, radiology (CT, MR, PET) or pathology AI training sets, metadata queries, visualization, or license checks — even when the user doesn't explicitly…
pkpd-modeling
Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when…
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
diffdock
DiffDock and DiffDock-L molecular docking. Use for protein-small-molecule pose prediction from PDB or sequence plus SMILES/SDF/MOL2, batch docking, virtual screening, and pose-confidence interpretation. Not for binding affinity prediction.
neuropixels-analysis
Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when…