Autoskill
Analyzes user-requested Screenpipe history windows to detect repeated research...
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill autoskill -g -a claude-code -y- Repository
- K-Dense-AI/scientific-agent-skills55.7k
- Category
- Data and ML
- Updated
- 9d ago
- License
- MIT license
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
Analyzes user-requested Screenpipe history windows to detect repeated research workflows, match existing scientific skills, and stage new skill drafts or composition recipes for review. Requires a reachable Screenpipe HTTP API, normally on localhost:3030. Detection and embedding inference run locally; the selected LLM receives redacted app/title cluster summaries and matched skill descriptions. Use only when the user explicitly asks to analyze their recent work and propose skills.
autoskill
Requires a running screenpipe daemon. This skill has no alternate data source, it reads exclusively from the local screenpipe HTTP API (default
http://localhost:3030). If the daemon isn't running,run()raisesScreenpipeUnreachablewith install instructions.
Network access & environment variables. This skill makes authenticated HTTP requests to (a) the user's local screenpipe daemon on loopback, and (b) the user-configured LLM backend, one of
http://localhost:1234/v1(LM Studio, default),https://api.anthropic.com(opt-in Claude), or a user-supplied BYOK Foundry gateway. The adapters readSCREENPIPE_TOKEN,LM_API_TOKEN,ANTHROPIC_API_KEY, andFOUNDRY_API_KEYfor the corresponding configured service. HTTPX also honors its standard proxy and CA environment settings; sentence-transformers uses Hugging Face cache/download settings. Opt-in cloud backends receive redacted cluster summaries and matched skill descriptions. Embedding-model installation may also download public model files; local inference does not imply zero network access.
Overview
Turn the user's own workflow history, captured passively by the local screenpipe daemon, into new skills. This skill is on-demand: the user invokes it with a time window, it queries screenpipe's local HTTP API, clusters repeated workflow patterns, compares each pattern against the existing skills in this repo, and produces a staged folder of proposals the user can review, edit, and promote.
When to Use This Skill
Invoke this skill when the user asks to:
- "Analyze my last 4 hours / day / week and propose new skills."
- "Look at what I've been doing and tell me what's not covered yet."
- "Draft a skill from my recent workflow."
- "Find composition recipes for workflows I repeat."
