by quchangle1 · LLM Plugin · ★ 489
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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 | 489 |
| Forks | 17 |
| Category | LLM Plugin |
| Quality Score | 56/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-09 |
| Created | 2024-04-25 |
| Est. Tokens | ~28k |
Survey: Tool Learning with Large Language Models Recently, tool learning with large language models(LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. This is the collection of papers related to tool learning with LLMs. These papers are organized according to our survey paper "Tool Learning with Large Language Models: A Survey".
These tools work well together with LLM-Tool-Survey for enhanced workflows:
Looking for a LLM-Tool-Survey alternative? If you're comparing LLM-Tool-Survey 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 things about LLM-powered agents. Papers / Repos / Blogs / ...
🤖 Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place.
A curated list of awesome resources, tools, and other shiny things for LLM prompt engineering.
The Open Assistant API is a ready-to-use, open-source, self-hosted agent/gpts orchestration creation framework
🦙 Integrating LLMs into structured NLP pipelines
The SmythOS Runtime Environment (SRE) is an open-source, cloud-native runtime for agentic AI. Secure, modular,
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: