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 YihongT · Codex Skill · ★ 63
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is CoAutoResearch safe to install? View the security audit →
An autonomous research partner that self-improves recursively and works with you. Define a question. Run experiments. Discuss findings. Refine the next step.Build a manuscript and paper draft from evidence you can trace. Quick start → · Features · Example papers · Documentation 🌐 Languages English · 简体中文 · 繁體中文 · 日本語 · 한국어 · EspañolPortuguês brasileiro · Français · Deutsch · Русский · العربية These are README translations. The interface and full documentation are in English; agent r
| Stars | 63 |
| Forks | 1 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 59.5011549299344/100 |
| Last Updated | 2026-09-27 |
| Created | 2026-06-16 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~15k |
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CoAutoResearch is An open-source research system for autonomous investigation, recursive self-improvement, and human–AI collaboration. Run experiments, refine methods through evidence and feedback, and turn traceable r. It is categorized as a Codex Skill with 63 GitHub stars.
CoAutoResearch is primarily written in Python. It covers topics such as agent-skills, agentic-ai, agentic-workflow.
You can find installation instructions and usage details in the CoAutoResearch GitHub repository at github.com/YihongT/CoAutoResearch. The project has 63 stars and 1 forks, indicating an active community.
CoAutoResearch is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to CoAutoResearch on Agent Skills Hub include Agon, AutoResearch-SibylSystem, agent-skills. 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: