by kouhxp · LLM Plugin · ★ 95
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/kouhxp/gutsy
pip install -e gutsy/gutsy-inference # needs llama-cpp-python >= 0.3.35Taken 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 | 95 |
| Forks | 4 |
| Language | HTML |
| Category | LLM Plugin |
| License | Apache-2.0 |
| Quality Score | 63/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-05 |
| Created | 2026-10-01 |
| Est. Tokens | ~18k |
gutsy Open, local decision models with calibrated probabilities. Send a state (text or JSON) and typed questions (yes/no, choice, score); get a probability for every option back. No text generation, no network calls, no per-call cost. The API follows the Jev-style Decisions format, so existing clients mostly need only a new base URL.
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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: