Awesome-OpenClaw-Research: Research-oriented collection of papers, analyses & resources on the OpenClaw ecosystem

by shuolucs · Codex Skill · ★ 340

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 54/100

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.

awesome-listopenclawopenclaw-agentopenclaw-researchopenclaw-security

Quick Facts

Stars340
Forks8
CategoryCodex Skill
LicenseMIT
Quality Score54/100
Open Issues3
Last Updated2026-07-05
Created2026-03-25
Est. Tokens~21k

About Awesome-OpenClaw-Research

Awesome-OpenClaw-Research 🦞 OpenClaw launched Nov 2025, hit 200k GitHub stars in 84 days, and surpassed 330k stars by March 2026. This repo collects papers studying or built upon the OpenClaw ecosystem — covering agent infrastructure, learning, safety, embodiment, social dynamics & domain applications. The questions are universal; OpenClaw is the lens. Table of Contents Papers —

Compatible Skills

These tools work well together with Awesome-OpenClaw-Research for enhanced workflows:

  • Awesome-OpenClaw — semantic(0.50)+complementary+rare_topics+similar_pop (52%)
  • AgentWard — semantic(0.40)+complementary+rare_topics+similar_pop (48%)
  • gemini-skill — semantic(0.40)+complementary+rare_topics+similar_pop (48%)
  • Aurogen — semantic(0.37)+complementary+similar_pop (48%)

Awesome-OpenClaw-Research alternative? Top 6 similar tools

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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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