by tomasonjo · Agent Tool · ★ 248
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Featured in: MCP Database Tools
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 | 248 |
| Forks | 42 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 72/100 |
| Open Issues | 3 |
| Last Updated | 2024-04-25 |
| Created | 2024-01-07 |
| Platforms | python |
| Est. Tokens | ~14k |
llm-movieagent This project is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using OpenAI function calling. The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph database based on the user's intent. Read more in the blog post.
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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: