Lapisan skill
untuk AI agents.
Biarkan AI agent Anda menemukan, membandingkan, dan memasang skill yang tepat secara otomatis.
Daily leaderboards
Skills moving now, ranked with evidence.
Daily snapshots combine capped activity signals, quality, trust, GitHub adoption, and real agent outcomes.
- 1web-automation
Scrapling
32 views, 2 install copies, and excellent quality signals.
70K stars507 events - 2data-analysis
Data Science For Beginners
3 views, 0 install copies, and 4 compares across 1 active days, with capped anti-spam weighting and 1 agent outcomes.
36K stars7 events / 1d - 3coding-agents
Gpt Pilot
30 views, 0 install copies, and excellent quality signals.
34K stars97 events - 4agent-frameworks
Claude Code Best Practice
23 views, 3 install copies, and excellent quality signals.
61K stars76 events - 5ml-automation
Ray
34 views, 1 install copies, and excellent quality signals.
43K stars81 events
Agent resolve
Jelaskan tugasnya. Dapatkan satu rencana skill yang aman.
Sebelum agent berjalan, API mengembalikan skill pilihan, alternatif, keputusan kebijakan, catatan audit, dan rencana pemasangan.
Kesesuaian tugas
96/100
Direkomendasikan untuk alur ekstraksi web
Pemeliharaan
Aktif
Stars, kebaruan, metadata, dan kesehatan repositori
Tinjauan pemasangan
Siap
Langkah aman sebelum agent dijalankan
Mengapa OpenAgentSkill
Jangan kirim agents ke direktori acak.
Registry skill hanya berguna ketika agent dapat mempercayainya. OpenAgentSkill mengubah proyek GitHub yang tersebar menjadi kemampuan yang dapat diperingkat, diaudit, dan dipasang.
- 01
Dari tugas ke skill
Agents mulai dari niat, bukan halaman kategori. Registry memetakan pekerjaan ke satu skill pilihan, alternatif, dan alasan kecocokan.
- 02
Keamanan sebelum pemasangan
Stars, kebaruan, skor kualitas, petunjuk izin, risiko, dan kesiapan berada di samping perintah yang akan dijalankan agent.
- 03
Manusia menjelajah, agents memakai API
Manusia dapat menelusuri indeks; agents dapat memanggil registry yang sama melalui endpoint resolve, recommendation, dan skill.
Respons registry
Satu panggilan, jalur pemasangan berperingkat.
{
"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"]
}
}
}Arsitektur
Empat lapisan antara niat dan pemasangan.
OpenAgentSkill bukan daftar statis lain. Ini adalah loop registry yang dapat dipanggil agent sebelum menulis file, membuka browser, atau memasang kode pihak ketiga.
Indexed
21,525
Signals
Fit · Risk
Surface
API · UI
- 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"
Perbandingan
Perbedaan OpenAgentSkill dari platform skill lain.
Pertaruhannya sederhana: direktori biasa dibuat untuk manusia. OpenAgentSkill dibuat agar AI agent dapat menemukan, membandingkan, dan memasang skill yang tepat secara otomatis.
| 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.
Web scraping
Monitor pricing and extract tables
Coding agents
Inspect repos, patch bugs, verify changes
RAG workflows
Turn documents into grounded answers
Workflow automation
Connect repeated ops across tools
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.