by Lolner95 · Agent Tool · ★ 147
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx @aigx/cli lint
npx @aigx/cli resolve src/features/auth/login.ts
Taken from the project's README on GitHub.
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.
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 | 147 |
| Forks | 28 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 64/100 |
| Last Updated | 2026-09-21 |
| Created | 2026-06-15 |
| Platforms | claude-code, python |
| Est. Tokens | ~17k |
The open, benchmark-validated context format for AI coding agents. A .aigx/ genome describes how your repo works — rules, per-file boundaries, gotchas — so any AI agent inherits your conventions and behaves like a senior engineer who already knows the code. Quick start · Install · The proof</
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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: