Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by InternLM · Codex Skill · ★ 523
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is WildClawBench safe to install? View the security audit →
WildClawBench []() []() Hard, practical, end-to-end evaluation for AI agents — in the wild. WildClawBench is an agent benchmark that tests what actually matters: can an AI agent do real work, end-to-end, without hand-holding? We drop agents into a live OpenClaw environment — the same open-source personal AI assistant that real users rely on daily — and throw 60 original tasks at them: clipping goal highlights from a football match, negotiating meeting times over multi-round emails, hunting down contradictions in search results, writing inference scripts for undocumented codebases, catching privacy leaks before they happen. Useful things. Hard things. Hard enough that every frontier model we tested scores below 0.55 (top overall 0.52). That makes scores mean something. Why WildClawBench? Most agent benchmarks test isolated capabilities — calling a function, parsing JSON, following a sing
| Stars | 523 |
| Forks | 64 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.2414881444168/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-18 |
| Created | 2026-03-23 |
| Platforms | python |
| Est. Tokens | ~18k |
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WildClawBench is An in-the-wild benchmark for AI agents in the production harness.. It is categorized as a Codex Skill with 523 GitHub stars.
WildClawBench is primarily written in Python. It covers topics such as agentic-ai, agentic-evaluation, agents.
You can find installation instructions and usage details in the WildClawBench GitHub repository at github.com/InternLM/WildClawBench. The project has 523 stars and 64 forks, indicating an active community.
WildClawBench is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to WildClawBench on Agent Skills Hub include NagaAgent, tools, awesome-openclaw. 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: