by vakovalskii · AI Tool · ★ 98
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
/plugin marketplace add vakovalskii/ontoship
/plugin install gitmark@ontoshipTaken 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 | 98 |
| Forks | 19 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 60/100 |
| Open Issues | 2 |
| Last Updated | 2026-10-09 |
| Created | 2026-06-06 |
| Platforms | claude-code, python |
| Est. Tokens | ~15k |
OntoShip A project knowledge base is just + a index + . Nothing simpler works better. Above: the actual documentation graph of a live production project (hub.neuraldeep.ru) — generated by , gravity mode on. Colors = services. This isn't a mockup; it's a KB being used and evolved daily. The idea Markdown is the source of truth — the thing you and your AI assistant read directly, in any editor.
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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: