by rentruewang · LLM Plugin · ★ 289
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install bocoel
pip install "bocoel[all]"Taken 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 | 289 |
| Forks | 16 |
| Language | Python |
| Category | LLM Plugin |
| License | BSD-3-Clause |
| Quality Score | 64/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-08 |
| Created | 2023-12-16 |
| Platforms | python |
| Est. Tokens | ~14k |
🫙 Archive This has topped the show HN front page link. This was born as a research project, and I think I have achieved most of what I set out to create. So today (2025/09/14) I'm archiving this repository. Nowadays I'm mainly working on , a deep learning algorithm compiler! Check it out.
Looking for a bocoel alternative? If you're comparing bocoel with other llm plugin tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, ma
The fastest way to build and start training your own LLM. CLI tool that scaffolds production-ready PyTorch tra
Dialectical reasoning architecture for LLMs (Thesis → Antithesis → Synthesis)
🦙 Integrating LLMs into structured NLP pipelines
A set of tools that gives agents powerful capabilities.
ToRA is a series of Tool-integrated Reasoning LLM Agents designed to solve challenging mathematical reasoning
Explore other popular llm plugin tools:
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: