by yetone · Codex Skill · ★ 19.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
go install github.com/yetone/magpie@latest
Taken from the project's README on GitHub. Check the security audit before running it.
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.
| Stars | 19,486 |
| Forks | 1,470 |
| Language | Go |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 63/100 |
| Open Issues | 1484 |
| Last Updated | 2026-10-22 |
| Created | 2026-09-23 |
| Platforms | claude-code, cli, codex, gemini, go |
| Est. Tokens | ~19k |
magpie One place to pick every agent's model: Codex on DeepSeek, Claude Code on Kimi, Gemini CLI on GLM, from the menu bar. usemagpie.ai is a single screen that lists each AI agent on your machine and the model it is set to. Click a value, pick a model. That is the whole app.
These tools work well together with magpie for enhanced workflows:
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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: