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SKILL LAYER · AGENT REGISTRY · AUTO INSTALLS
The skill layer
for AI agents.
Let your AI agent find, compare, and install the right reusable skill automatically.
Browse all skills
Connect my AI
OpenAgentSkill Codex / Claude Code / Cursor
Paste into Codex, Claude Code, or Cursor. Copying does not install anything.
Setup guide ↗Made with skills
Your next project starts here.
See the result. Copy a task. Make it yours with the skill behind it.
Categories
Explore by task
Start with what you want to do.
GitHub popularity leaderboard
The most-starred agent skill projects.
Popularity starts with GitHub stars, then OpenAgentSkill filters out weak skill matches and keeps trust signals visible before install.
Updated Oct 12, 02:55 AM UTCView all rankings
- 1
391K stars and excellent quality signals.
- 2
285K stars and excellent quality signals.
- 3
280K stars and excellent quality signals.
- 4
272K stars and excellent quality signals.
- 5
251K stars and strong quality signals.
- 6
180K stars and excellent quality signals.
- 7
175K stars and excellent quality signals.
- 8
136K stars and excellent quality signals.
- 9
120K stars and excellent quality signals.
- 10
107K stars and excellent quality signals.
Why OpenAgentSkill
Stop sending agents into random directories.
A skill registry only becomes useful when an agent can trust it. OpenAgentSkill turns scattered GitHub projects into ranked, auditable, install-ready capabilities that can be called from Codex, Claude Code, Cursor, MCP-compatible agents, and custom runtimes.
- 01
Task-to-skill resolution
Agents start with intent, not category pages. The registry maps a job to one selected skill, alternatives, and fit reasons.
- 02
Safety before install
Stars, freshness, quality score, permission hints, risks, and readiness notes sit beside the command an agent will run.
- 03
Human browse, agent API
People can browse the index; agents can call the same registry through resolve, recommendation, and skill endpoints.
Registry response
One call, ranked install path.
{
"task": "analyze stock news",
"agent_decision": {
"recommended_skill": "Last30days Skill",
"install_command": "npx skills add ...",
"why_recommended": [
"matches research workflow",
"strong Trust Score",
"audit warnings included"
],
"risk_summary": {
"safety": "review before install",
"notes": ["network access", "verify sources"]
}
}
}Architecture
Four layers between intent and install.
OpenAgentSkill is not another static list; it is a registry loop an agent can call before it writes files, opens browsers, or installs third-party code.
Projects found
26,302
Validated skills
35,724
Installable
35,175
- 01
Intent capture
A human or upstream agent describes the job in natural language.
Task · Agent · ContextIntent - 02
Recommendation engine
rankerSkills are ranked by workflow fit, maintenance, stars, and audit signals.
Fit · Quality · FreshnessRank - 03
Skill trust profile
Each candidate gets readiness notes, install commands, and review prompts.
Risk · Install · EvidenceAudit - 04
Agent install path
The registry returns the next action an agent can safely execute.
Codex · Claude Code · CursorInstall
Quickstart
From task description to install command.
- 01
Ask for a skill path
Resolve the task into one selected skill, alternatives, safety score, and install plan.
POST /api/agent/resolve - 02
Inspect the trust profile
Review fit, repository health, risks, and install readiness.
GET /api/agent/skills/crawl4ai - 03
Install in an agent workflow
Copy the command or hand it to Codex, Claude Code, Cursor, or a custom agent.
GET /api/skills/crawl4ai/install?format=text - 04
Automate discovery
Use the API as the registry layer behind your own agent runtime.
curl "https://www.openagentskill.com/api/agent/resolve?task=review+pull+requests&agent=codex"
Agent surfacesCodex, Claude Code, Cursor, MCP-compatible agents, and custom internal runners.
Compare
How OpenAgentSkill differs from other skill platforms.
The big bet is simple: ordinary directories are for people to browse. OpenAgentSkill is built so an AI agent can discover, compare, and install the right skill automatically.
| Feature | OpenAgentSkill | skills.sh | agentskills.io | Native docs |
|---|---|---|---|---|
| Primary job | Recommend, compare, and install skills from one registry | Browse and install reusable agent skills | Define the open skill format and learning path | Explain skills inside each native agent platform |
| Agent-facing API | Yes - task-to-skill recommendations for agents | Directory and install workflow | Spec and documentation first | Platform-specific APIs and docs |
| Cross-agent positioning | Codex, Claude Code, Cursor, MCP-compatible agents, and custom tools | Open agent skills ecosystem | Open format for extending agents | Best for the vendor platform |
| Trust and audit signals | Stars, quality score, readiness notes, install review | Directory metadata | Metadata guidance in SKILL.md | Native platform controls |
| Best for | Letting an agent find the right skill automatically | Finding installable skills quickly | Learning or authoring the standard | Using skills in one product |
Comparison is based on each project’s public positioning and documentation. The point is not that one project replaces another; OpenAgentSkill focuses on the registry and recommendation layer agents can call.
Workflow starts
Start from the job your agent needs to do.
01Get recommendations02Get recommendations03Get recommendations04Get recommendations
Web scraping
Monitor pricing and extract tables
Crawl4AI
Firecrawl
Browser automation
Coding agents
Inspect repos, patch bugs, verify changes
GitHub
Playwright
Code review
RAG workflows
Turn documents into grounded answers
MarkItDown
LlamaIndex
Vector search
Workflow automation
Connect repeated ops across tools
n8n
Zapier
Scheduled agents
Skill layer
Registry for humans. Skill layer for agents.
Browse when you are exploring. Call the recommendation API when your agent needs to pick, compare, and install a skill automatically.
