ResearcherSkill: Asks what to optimise and how to measure it, then runs experiments on its own branch

by krzysztofdudek · Codex Skill · ★ 268

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Security audit verdict: SAFE · quality 61/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsai-codingarchitecture-as-codeautonomousautoresearchclaude-codecodexcursordeveloper-toolsexperimentation

Quick Facts

Stars268
Forks27
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score61/100
Last Updated2026-11-04
Created2026-03-22
Platformsclaude-code, cli, codex, gemini, python
Est. Tokens~14k

About ResearcherSkill

Researcher Skill One file. Your AI coding agent becomes a scientist. Drop into Claude Code, Codex, or any agent. It will design experiments, test hypotheses, discard what fails, keep what works — 30+ experiments overnight while you sleep.

Compatible Skills

These tools work well together with ResearcherSkill for enhanced workflows:

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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