by OnlyTerp · Codex Skill · ★ 389
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Flagged: reads agent memory. 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.
| Stars | 389 |
| Forks | 42 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 6 |
| Last Updated | 2026-07-01 |
| Created | 2026-03-13 |
| Platforms | claude-code, gemini, node |
| Est. Tokens | ~22k |
OpenClaw Optimization Guide Make Your OpenClaw AI Agent Faster, Smarter, and Actually Useful Speed optimization, memory architecture, context management, model selection, and one-shot development for OpenClaw By Terp — Terp AI Labs Table of Contents Speed — Trim context files, add fallbacks, manage reasoning mode Context Bloat — Quadratic scaling, built-in defenses Cron Session Bloat — Session...
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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: