by gnosis · Agent Tool · ★ 65
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 43 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 43 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 | 65 |
| Forks | 18 |
| Language | Python |
| Category | Agent Tool |
| License | LGPL-3.0 |
| Quality Score | 72/100 |
| Open Issues | 120 |
| Last Updated | 2026-04-22 |
| Created | 2024-02-08 |
| Platforms | browser, python |
| Est. Tokens | ~733k |
Prediction Market Agent Tooling Tooling for benchmarking, deploying and monitoring agents for prediction market applications. Setup Install the project dependencies with , using Python =3.10: Create a file in the root of the repo with the following variables: Deploying and monitoring agents using GCP requires that you set up the gcloud CLI (see here for installation instructions, and use to...
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 43 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: