skillnet
Search, download, create, evaluate, and analyze reusable agent skills via SkillNet — the open skill supply chain for AI agents. Use when: (1) Before any multi-step task — search SkillNet for existing skills first, (2) User says "find a skill", "learn this repo/doc", "turn this into a skill", or mentions skillnet, (3) User provides a GitHub URL, PDF, DOCX, PPT, execution logs, or trajectory — create a skill from it, (4) After completing a complex task with non-obvious solutions — create a skill to preserve learnings, (5) User wants to evaluate skill quality, organize/analyze a local skill libra
npx skills add zjunlp/SkillNet --skill skillnet --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SkillNet Search a global skill library, download with one command, create from repos/docs/logs, evaluate quality, and analyze relationships. ## Platform-Neutral Use This skill is portable across agents that understand `SKILL.md` directories, including Codex, Claude Code, OpenClaw, and similar tools. Use a single placeholder, `<skills-dir>`, for the local skill library: - If the user or host agent provides a skills directory, use that. - Else if `SKILLNET_SKILLS_DIR` is set, use it. - Else use the active agent's conventional directory: Codex `$CODEX_HOME/skills` or `~/.codex/skills`, Claude Code `~/.claude/skills`, OpenClaw `~/.openclaw/workspace/skills`. - If none is clear, ask once or use `./generated_skills` for newly created skills. Never hard-code one agent's directory in reusable commands. In shell examples below, replace `<skills-dir>` with the selected directory. ## Core Principle: Search Before You Build — But Don't Block on It SkillNet is your skill supply chain. Before starting any non-trivial task, **spend 30 seconds** searching — someone may have already solved your exact problem. But if results are weak or absent, proceed immediately with your own approach. The searc
- Platform-Neutral Use
- Core Principle: Search Before You Build — But Don't Block on It
- Process
- Step 1: Pre-Task Search
- Step 2: Download → Load → Apply
- Capabilities
- Create a Skill
- Evaluate Quality
- Analyze & Maintain Library
- Orchestrate a Scene
- In-Task Triggers
- Environment Variables
- Resource Navigation
- Security Essentials
skillnet search "langgraph" --limit 5 If 0 or irrelevant → try the task pattern skillnet search "multi-agent" --limit 5 If still 0 → one retry with vector mode (longer queries OK here) skillnet search "multi-agent supervisor orchestration" --mode vector --threshold 0.65 Download to local skill library (GitHub URLs only) skillnet download "<skill-url>" -d "<skills-dir>" ls -la "<skills-dir>/<skill-name>/" head -20 "<skills-dir>/<skill-name>/SKILL.md" cat "<skills-dir>/<skill-name>/SKILL.md"
What does the skillnet skill do?
Search, download, create, evaluate, and analyze reusable agent skills via SkillNet — the open skill supply chain for AI agents. Use when: (1) Before any multi-step task — search SkillNet for existing skills first, (2) User says "find a skill", "learn this repo/doc", "turn this into a skill", or mentions skillnet, (3) User provides a GitHub URL, PDF, DOCX, PPT, execution logs, or trajectory — create a skill from it, (4) After completing a complex task with non-obvious solutions — create a skill to preserve learnings, (5) User wants to evaluate skill quality, organize/analyze a local skill libra
How do I install it?
Run `npx skills add zjunlp/SkillNet --skill skillnet --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From zjunlp/SkillNet, a repository with 1,117 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.