ClawProBench: Live-first benchmark harness for evaluating LLM agents in the OpenClaw runtime with deterministic grading

by suyoumo · Codex Skill · ★ 829

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

Security audit verdict: SAFE · quality 68/100

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. Full audit →

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.

agentbenchmarkevaluationharnessleaderboardllmopenclaw

Quick Facts

Stars829
Forks54
LanguageRust
CategoryCodex Skill
LicenseApache-2.0
Quality Score68/100
Last Updated2026-08-25
Created2025-03-02
Platformsrust
Est. Tokens~23k

About ClawProBench

ClawProBench Transparent live-first benchmark harness for evaluating model capability inside the OpenClaw runtime. 102 active scenarios, 162 catalog scenarios, deterministic grading, and OpenClaw-native coverage. ClawProBench focuses on real OpenClaw execution with deterministic grading, structured reports, and benchmark-profile selection.

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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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