No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by besoeasy · MCP Server · ★ 132
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is open-skills safe to install? View the security audit →
Open Skills Battle-tested execution playbooks that give any AI agent the exact commands, APIs, and patterns it needs — cutting token usage by 95–98% and making local models as capable as GPT-4. The Problem AI agents waste tokens discovering what you already know: Cloud models (GPT-4, Claude) — 10–30 trial-and-error calls per task → $0.15–$0.25/task Local models (Llama, Mistral, Qwen) — often fail outright without step-by-step guidance The Solution Pre-written, tested skill files your agent reads once and executes correctly the first time. Quick Start Copy the contents of into your agent's system prompt, memory, or instructions file. This tells the agent to check before every task and auto-
| Stars | 132 |
| Forks | 32 |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.1457909722602/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-04 |
| Created | 2026-02-13 |
| Platforms | browser, claude-code, mcp |
| Est. Tokens | ~18k |
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open-skills is Battle-tested skill library for AI agents. Save 98% of API costs with ready-to-use code for crypto, PDFs, search, web scraping & more. No trial-and-error, no expensive APIs.. It is categorized as a MCP Server with 132 GitHub stars.
You can find installation instructions and usage details in the open-skills GitHub repository at github.com/besoeasy/open-skills. The project has 132 stars and 32 forks, indicating an active community.
open-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to open-skills on Agent Skills Hub include agent-skills, kindly-web-search-mcp-server, agent-skills-cli. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: