by run-llama · Agent Tool · ★ 84
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx skills add run-llama/llamaparse-agent-skills
npx skills add run-llama/llamaparse-agent-skills --skill llamaparse # or the name of another skillTaken from the project's README on GitHub.
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 | 84 |
| Forks | 10 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 55/100 |
| Last Updated | 2026-07-03 |
| Created | 2026-03-02 |
| Platforms | python |
| Est. Tokens | ~4k |
LlamaParse Skills Unblock document intelligence for your agents with LlamaParse skills. Available Skills llamaparse Skill for unstructured documents parsing and text-extraction leveraging LlamaParse. Use with: PDFs, presentations, Word documents, spreadsheets, images Complex documents which need advanced parsing capabilties in order to get their content extracted Documents with images, charts and...
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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: