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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 | 83 |
| Forks | 15 |
| Language | Shell |
| Category | AI Tool |
| License | MIT |
| Quality Score | 53/100 |
| Last Updated | 2026-06-19 |
| Created | 2026-06-19 |
| Platforms | claude-code, cli |
| Est. Tokens | ~3k |
LLM Wiki Made by gptaku · Threads @gptakuai raw 소스를 LLM이 직접 합성·유지하는 영구 마크다운 위키로 키우는 워크스페이스입니다. Karpathy의 "LLM Wiki" 패턴을 Claude Code 위에 구현했습니다. 매 질문마다 원문을 재검색하는 RAG와 달리, 한 번 합성하고 최신 상태로 누적합니다. 사람은 소싱·탐색·질문을 하고, LLM이 위키의 모든 쓰기·정리·교차참조를 담당합니다. 외부 스킬·플러그인 의존이 없습니다. 이 폴더만 있으면 어떤 Claude Code 환경에서든 동작합니다. 받아서 자기 데이터로 채우고, 쓰면서 고도화하는 스타터입니다 — 정답이 아니라 시작점. 시작하기 처음 실행하면 사용법 안내가 자동으로 뜹니다 (SessionStart 훅).
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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: