No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by krzysztofdudek · Codex Skill · ★ 261
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ResearcherSkill safe to install? View the security audit →
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. What it looks like running ### Experiment 5 — Parallelize independent test suites Branch: research/faster-tests · Parent: #3 · Type: real Hypothesis: Unit and integration suites don't share state. Running them in parallel should cut total time. Changes: split test config into two parallel jobs in Result: 38s (was 94s baseline, 52s best) — new best Status: keep Insight: Most of the remaining time is in integration tests. Unit tests finish in 6s. Focus on integration from here.
| Stars | 261 |
| Forks | 27 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 58.6623827183754/100 |
| Last Updated | 2026-09-12 |
| Created | 2026-03-22 |
| Platforms | claude-code, cli, codex, gemini, python |
| Est. Tokens | ~12k |
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ResearcherSkill is One file. Your AI coding agent becomes a scientist. 30+ experiments while you sleep.. It is categorized as a Codex Skill with 261 GitHub stars.
ResearcherSkill is primarily written in Python. It covers topics such as ai-agents, ai-coding, architecture-as-code.
You can find installation instructions and usage details in the ResearcherSkill GitHub repository at github.com/krzysztofdudek/ResearcherSkill. The project has 261 stars and 27 forks, indicating an active community.
ResearcherSkill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ResearcherSkill on Agent Skills Hub include quint-code, agent-skills, compose-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: