Autoskill

---
name: autoskill
description: Observe 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.
allowed-tools: Read Write Edit Bash
license: MIT license
required_environment_variables: [{"name": "SCREENPIPE_TOKEN", "prompt": "Auth token for the local screenpipe daemon.", "required_for": "full functionality"}, {"name": "ANTHROPIC_API_KEY", "prompt": "For Claude API calls during skill drafting.", "required_for": "optional features"}, {"name": "FOUNDRY_API_KEY", "prompt": "Optional Foundry access for drafting.", "required_for": "optional features"}]
metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"requires": {"bins": ["screenpipe"]}, "primaryEnv": "SCREENPIPE_TOKEN", "envVars": [{"name": "SCREENPIPE_TOKEN", "required": true, "description": "Auth token for the local screenpipe daemon."}, {"name": "ANTHROPIC_API_KEY", "required": false, "description": "For Claude API calls during skill drafting."}, {"name": "FOUNDRY_API_KEY", "required": false, "description": "Optional Foundry access for drafting."}]}}
---

# 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 skill reads three environment variables — `SCREENPIPE_TOKEN`, `ANTHROPIC_API_KEY`, `FOUNDRY_API_KEY` — and uses each only to authenticate to the single endpoint its name implies. No other network destinations, no telemetry, no data egress to any third party.

## 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."