by lunary-ai · Agent Tool · ★ 56
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm install abso-aiTaken from the project's README on GitHub.
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 | 56 |
| Forks | 8 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 71/100 |
| Open Issues | 4 |
| Last Updated | 2025-06-02 |
| Created | 2025-02-08 |
| Platforms | gemini, node |
| Est. Tokens | ~13k |
Drop-in replacement for OpenAI Abso provides a unified interface for calling various LLMs while maintaining full type safety. Features OpenAI-compatible API 🔁 (drop in replacement) Call any LLM provider (OpenAI, Anthropic, Groq, Ollama, etc.) Lightweight & Fast ⚡ Embeddings support 🧮 Unified tool calling 🛠️ Tokenizer and cost calculation (soon) 🔢 Smart routing (soon) Providers ❌
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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: