# Autoskill

Analyzes user-requested Screenpipe history windows to detect repeated research...

- Repository: https://github.com/K-Dense-AI/scientific-agent-skills (55651 stars)
- Category: Data and ML
- License: MIT license
- Last updated: 2026-10-26
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add K-Dense-AI/scientific-agent-skills --skill autoskill -g -a claude-code -y`
Codex: `npx skills add K-Dense-AI/scientific-agent-skills --skill autoskill -g -a codex -y`
Cursor: `npx skills add K-Dense-AI/scientific-agent-skills --skill autoskill -g -a cursor -y`
Gemini CLI: `npx skills add K-Dense-AI/scientific-agent-skills --skill autoskill -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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."
- "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."

The rest: https://github.com/K-Dense-AI/scientific-agent-skills/blob/HEAD/skills/autoskill/SKILL.md