Openagentskill
Skill registry for AI agents: discover, audit, and install reusable skills.
A solid agent-native registry that solves a real problem: discovering safe, installable skills without manual GitHub hunting. Its trust profiles and recommendation engine add genuine value over raw directories. However, adoption is limited by CLI/API-only usage and no execution runtime.
- AI agent developers needing a registry to discover and install skills automatically
- DevOps engineers automating workflows with agent-native skill discovery
- Researchers building agentic RAG pipelines that require curated skill layers
- Startups creating custom agent runtimes that need an API-driven skill index
- End-users seeking out-of-the-box AI apps with no CLI or API interaction
- Teams wanting a managed skill execution environment (no runtime provided)
- Non-developers unfamiliar with CLI tools or API calls
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In short
Openagentskill — Skill registry for AI agents: discover, audit, and install reusable skills. Best for AI agent developers needing a registry to discover and install skills automatically, DevOps engineers automating workflows with agent-native skill discovery, Researchers building agentic RAG pipelines that require curated skill layers. Free to use.
Viability Score
How likely is Openagentskill to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Task-to-skill resolution from natural language description
- Recommendation engine ranks by fit, freshness, stars, quality
- Trust profiles with risk summary, permissions, audit notes
- Install plan generation with ready-to-run commands
- 20K+ indexed skills across categories
- API endpoints: resolve, skills list, skill details, install path
- CLI installation: npx skills add owner/repo
- Skill submission via GitHub with skill.json manifest
- Category browsing (productivity, devops, finance, etc.)
- Anonymous telemetry for leaderboard ranking
- Format=text parameter for LLM-optimized responses
- Integration with Codex, Claude Code, Cursor, MCP agents
- Supports web scraping, coding, RAG, browser automation skills
About Openagentskill
OpenAgentSkill is a skill registry for AI agents, designed to let agents find, compare, and install reusable capabilities automatically. It turns scattered GitHub projects into ranked, auditable, install-ready skills that can be called from Codex, Claude Code, Cursor, MCP-compatible agents, and custom runtimes. The platform indexes over 20,000 skills and has registered 860K+ downloads across 104 agent surfaces. The core workflow starts with a natural language task description; the registry's recommendation engine ranks skills by workflow fit, maintenance, stars, and audit signals. Each candidate includes readiness notes, install commands, and review prompts before the agent executes. OpenAgentSkill differentiates itself by focusing on safety and trust. It provides a skill trust profile for each candidate, including risk summaries, permission hints, and audit notes. The architecture has four layers: intent capture, recommendation engine ranking, skill trust profiling, and agent install path. The API returns a selected skill, alternatives, policy decisions, and install plans before an agent acts. It serves developers building agentic workflows who need a reliable way to discover and integrate third-party skills without manually browsing GitHub. The registry is browsable by humans but fully accessible via API for agent native discovery. Compared to skills.sh or agentskills.io, OpenAgentSkill emphasizes agent-facing recommendation APIs and cross-agent compatibility across Codex, Claude Code, Cursor, and MCP environments.
Behind the Verdict
OpenAgentSkill fills a niche that's been quietly painful: finding and vetting third-party capabilities for your AI agent without digging through stale GitHub repos. The registry's recommendation engine is the key differentiator—it ranks by fit, freshness, stars, and audit signals, not just popularity. We've seen a few agent-native directories come and go, but this one actually returns an install plan with risk notes, which is what a cautious developer needs before letting an agent run someone else's code. The 20K+ skill index and 860K+ downloads suggest real traction, especially in the open-source agent ecosystem. Where it falls short is the lack of a managed runtime—you still need your own execution environment. If you're building with Codex, Claude Code, or Cursor, the integration is straightforward via CLI and API. But if you're a non-developer hoping for a one-click agent skill store, you'll hit a wall. The trust profiles are a step up from raw GitHub searches, but they're still based on metadata (stars, freshness, quality score) rather than sandboxed execution, so you can't assume zero risk. Compared to skills.sh, which is more of a human-browsable directory, OpenAgentSkill's API-first approach makes it better for embedding in automated workflows. For teams already using MCP-compatible agents, the install path integration is clean. Overall, it's a useful tool for agent developers who value safety signals over convenience, but not a complete solution for non-technical users.
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Use Cases
- Resolve a web scraping task to the best skill with installation command and safety notes.
- Discover coding agent skills for pull request review and code generation.
- Find RAG skills to ingest documents and retrieve grounded context.
- Automate browser tasks by selecting and installing browser automation skills.
- Compare skill options for financial data analysis before running any code.
- Integrate the registry API into a custom agent runtime for dynamic skill discovery.
Limitations
- The registry does not execute skills itself—it only provides discovery, audit, and install commands.
- Skill quality and security depend on community submissions and routine audits, but OpenAgentSkill cannot guarantee every skill's safety.
- The telemetry-based ranking may not reflect real-world performance in all agent setups.
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