Individual skill
autoskill
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.
skills/autoskill/SKILL.md
Source commit a25cf9ec00e2745d03f8704acf6f6919694ccd9a
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 3, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
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An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 56,484
- Repository last updated
- Nov 2, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-scientist Repository topic
- bioinformatics Repository topic
- chemoinformatics Repository topic
- claude Repository topic
- claude-skills Repository topic
- claudecode Repository topic
- clinical-research Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- drug-discovery Repository topic
- research Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
.gitignoreasset · 34 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesSKILL.mdcore · 16,912 bytesconfig.yamlasset · 2,172 bytesreferences/api-contracts.mddoc · 7,579 bytesreferences/https-proxy.mddoc · 2,222 bytesreferences/screenpipe-config.yamlasset · 1,410 bytesscripts/autoskill.pyruns · 1,158 bytesscripts/backends.pyruns · 5,268 bytesscripts/cluster.pyruns · 1,723 bytesscripts/doctor.pyruns · 4,602 bytesscripts/fetch_window.pyruns · 2,863 bytesscripts/match_skills.pyruns · 1,274 bytesscripts/promote.pyruns · 1,559 bytesscripts/redact.pyruns · 1,647 bytesscripts/run.pyruns · 7,579 bytesscripts/synthesize.pyruns · 2,785 bytes
Read excerpt
# autoskill
> **Requires a running [screenpipe](https://github.com/screenpipe/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()` raises `ScreenpipeUnreachable` with 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 read `SCREENPIPE_TOKEN`, `LM_API_TOKEN`, `ANTHROPIC_API_KEY`, and `FOUNDRY_API_KEY` for 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](https://github.com/screenpipe/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."