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 ymx10086 · Codex Skill · ★ 311
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ResearchClaw safe to install? View the security audit →
ResearchClaw Local-first Research OS for papers, workflows, experiments, channels, and automation. English Research-Equality Ecosystem Part of the Research-Equality ecosystem for AI-native research workflows. Persistent research state · Multi-agent runtime · Skills + MCP · Automation + channels Why ResearchClaw • Quick Start • Research-Equality Ecosystem • What You Get Today • Docs Why ResearchClaw ResearchClaw is t
| Stars | 311 |
| Forks | 36 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 63.9581209355283/100 |
| Open Issues | 12 |
| Last Updated | 2026-04-04 |
| Created | 2026-03-03 |
| Platforms | python |
| Est. Tokens | ~595k |
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ResearchClaw is ResearchClaw is a personal AI assistant built for research: fast to set up, easy to run locally or in the cloud, and ready to integrate with the chat apps you already use. With extensible skills, it h. It is categorized as a Codex Skill with 311 GitHub stars.
ResearchClaw is primarily written in Python. It covers topics such as agent, copaw, openclaw.
You can find installation instructions and usage details in the ResearchClaw GitHub repository at github.com/ymx10086/ResearchClaw. The project has 311 stars and 36 forks, indicating an active community.
The top alternatives to ResearchClaw on Agent Skills Hub include OpenAEON, starpod, awesome-ai-sdks. 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: