by albertofettucini · Agent Tool · ★ 117
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git clone https://github.com/albertofettucini/Council.gitTaken 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 | 117 |
| Forks | 8 |
| Language | Swift |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 66/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-14 |
| Created | 2026-06-06 |
| Platforms | claude-code, gemini |
| Est. Tokens | ~16k |
Council One question. A roundtable of AI minds. You decide. A native macOS app that puts the same question to several LLMs, lets them critique each other blind, and shows you where they agree — and where they don't. The idea Asking one model a hard question gives you one model's blind spots. Council convenes a panel instead.
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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: