by agentscope-ai · Claude Skill · ★ 131
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/agentscope-ai/agentscope-skills.gitTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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 | 131 |
| Forks | 14 |
| Language | Python |
| Category | Claude Skill |
| License | Apache-2.0 |
| Quality Score | 63/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-10 |
| Created | 2026-02-12 |
| Platforms | claude-code, python |
| Est. Tokens | ~5k |
Skills A curated collection of skills around AgentScope ecosystem and QwenPaw applications — ready to be installed into AI coding assistants like Claude Code, Cursor, and more. Each skill is a self-contained knowledge pack that gives your AI assistant deep, accurate context about AgentScope: its APIs, design patterns, examples, and best practices.
These tools work well together with skills for enhanced workflows:
Looking for a skills alternative? If you're comparing skills with other claude skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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MCP server wrapper for OpenAI Codex CLI that enables Claude Code to leverage Codex's AI capabilities directly.
MCP tool: let you point at DOM elements for your favorite agentic coding tool. Let AI see what you see.
The missing linter and lsp for AI coding assistants. Validate CLAUDE.md, AGENTS.md, SKILL.md, hooks, MCP. Plug
AI agent tooling for data engineering workflows.
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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: