by asakin · LLM Plugin · ★ 74
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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 | 74 |
| Forks | 9 |
| Language | Python |
| Category | LLM Plugin |
| License | Apache-2.0 |
| Quality Score | 63/100 |
| Last Updated | 2026-07-03 |
| Created | 2026-04-08 |
| Platforms | claude-code, python |
| Est. Tokens | ~15k |
[Project Name] This README will be personalized during your first session. llm-context-base An opinionated template for building your own LLM Wiki. Use the template, point your AI assistant at it, and start talking. Over time, it learns how you work and adapts its structure to your needs without you having to design anything upfront.
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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: