longevity-scholar

longevity-scholar is a skill for Claude Code, Codex from Zaoqu-Liu/ScienceClaw. It costs 72 tokens per session (1,903 once invoked), scanned A, original, MIT.

A research-search skill for finding academic papers about longevity, aging, lifespan extension, and healthspan. It uses the Semantic Scholar API to find and summarize relevant papers.

In plain words
What is it for?
Finding recent, highly cited, or relevant papers, including work by specific researchers or studies of particular compounds or organisms. It is not intended for general longevity questions without a paper-search request.
Why use it?
It narrows research searches to longevity topics and helps turn paper results into plain-language summaries. It is meant for requests that explicitly ask for academic literature or research papers.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 scripts/query_longevity_papers.py \.

Good fit Finding recent, highly cited, or relevant papers, including work by specific researchers or studies of particular compounds or organisms. It is not intended for general longevity questions without a paper-search request.

Compare 6 skills from other repositories ↓
Install

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.

Clone the repo
git clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClaw
agentmods
npx agentmods add skills/zaoqu-liu/scienceclaw/bioagent-longevity-scholar

Made for: Claude Code, Codex.

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 longevity-scholar

README.md
[![agentmods](https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/bioagent-longevity-scholar/github.svg)](https://agentmods.dev/skills/zaoqu-liu/scienceclaw/bioagent-longevity-scholar)
Your own site
<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.

agentmods 80×15 button for longevity-scholar

Your own site · 80×15
<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>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,903 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.
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 $0.00072 $0.01903
Opus 5 $0.00036 $0.00951
Sonnet 5 $0.00014 $0.00381
Haiku 4.5 $0.00007 $0.00190

Measured 10d ago against content hash 83c13aecce38, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

skills/bioagent-longevity-scholar/SKILL.md · 170 lines

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:

  1. Main topic (e.g., "longevity experiments on mice")
  2. Filters (e.g., "most cited", "recent", "past 2 weeks", "by Aubrey De Grey")
  3. 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:

  1. Check current date from the <env> block at the start of the conversation
  2. 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
  3. Format as YYYY-MM-DD: for the --date-filter parameter

Read the full file on GitHub · 170 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. 10d ago First seen · 170 lines · 72 tokens per session scan A 83c13aecce38

Subscribe to this mod's changes

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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