Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawnpx agentmods add skills/zaoqu-liu/scienceclaw/bioagent-longevity-scholarWrote 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/zaoqu-liu/scienceclaw/bioagent-longevity-scholar)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/bioagent-longevity-scholar"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/bioagent-longevity-scholar/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/zaoqu-liu/scienceclaw/bioagent-longevity-scholar"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/bioagent-longevity-scholar.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00072 | $0.01903 |
| Opus 5 | $0.00036 | $0.00951 |
| Sonnet 5 | $0.00014 | $0.00381 |
| Haiku 4.5 | $0.00007 | $0.00190 |
Grade A, and why
longevity-scholar 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 10d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Longevity Scholar Skill
Purpose
This skill enables targeted searches of longevity and aging research using the Semantic Scholar API. It provides a simple, focused interface for finding relevant academic papers and synthesizing research findings into natural language responses.
When to Use This Skill
Use this skill when users explicitly request academic papers about longevity topics, including:
- Latest/recent research findings on longevity experiments
- Most cited research papers on longevity interventions
- Papers by specific longevity researchers
- Relevant research papers testing specific compounds or organisms
- Academic literature on aging, lifespan extension, healthspan, etc.
Trigger phrases:
- "What are the latest findings on..."
- "What are the most cited research papers on..."
- "What are the most recent research papers on..."
- "What are the most relevant research papers on..."
- "Find papers about..."
- "Show me research on..."
Do NOT trigger on general questions about longevity that don't explicitly request academic papers or research.
How to Use This Skill
Step 1: Understand the Query
Parse the user's request to identify:
- Main topic (e.g., "longevity experiments on mice")
- Filters (e.g., "most cited", "recent", "past 2 weeks", "by Aubrey De Grey")
- Specific focus (e.g., "rapamycin", "flies", "caloric restriction")
Important: Handle date-based queries
When users request papers from specific time periods (e.g., "past 2 weeks", "last month", "recent papers"), calculate the appropriate date range:
- Check current date from the
<env>block at the start of the conversation - Calculate the start date based on the time period requested:
- "Past 2 weeks" → subtract 14 days from current date
- "Past month" → subtract 30 days from current date
- "Past 3 months" → subtract 90 days from current date
- "Recent" or "latest" (no specific period) → use past 2-3 years
- Format as YYYY-MM-DD: for the --date-filter parameter
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.
- 10d ago First seen · 170 lines · 72 tokens per session scan A 83c13aecce38
longevity-scholar is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 72 tokens to every session and 1,903 once invoked, about $0.0004 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 2026-08-30.
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