Autoskill
Skill Verifiziert AktivObserve the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
To automate the creation of new AI agent skills by analyzing actual user workflows, saving time and ensuring skills align with practical research needs.
Funktionen
- Observe user screen activity via screenpipe
- Detect and cluster repeated workflow patterns
- Match workflows against existing agent skills
- Draft new skills or composition recipes
- Local data processing and redaction for privacy
Anwendungsfälle
- Analyze recent work to propose new skills
- Draft skills based on observed user workflows
- Find composition recipes for repeated tasks
- Identify gaps in existing AI agent capabilities
Nicht-Ziele
- Real-time screen queries
- Analyzing screenpipe itself
- Running without user explicit trigger
- Sending raw screen data to LLM or third parties
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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