/autoskill

name: autoskill
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.
allowed-tools: Read Write Edit Bash
license: MIT license
compatibility: Requires Python 3.10+ with httpx, PyYAML, and sentence-transformers; Screenpipe and a local LM Studio server or an opt-in cloud LLM. Initial model installation needs network access.
metadata:
  version: "1.6"
  last-reviewed: "2026-09-30"
  skill-author: K-Dense Inc.
  openclaw:
    primaryEnv: SCREENPIPE_TOKEN
    envVars:
    - name: SCREENPIPE_TOKEN
      required: false
      description: Bearer token when the selected Screenpipe daemon has authentication enabled.
    - name: LM_API_TOKEN
      required: false
      description: Bearer token if the selected LM Studio server requires authentication.
    - 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 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."

Do **not** invoke it for one-off questions about screenpipe itself, for real-time screen queries, or without an explicit user request — the skill analyzes sensitive local content and must stay explicitly user-triggered.

Privacy Posture

• **Configure capture filtering in Screenpipe before collecting history.** `references/screenpipe-config.yaml` is a checklist of literal, case-insensitive app/title substrings, not a Screenpipe-importable YAML file. Apply them in Screenpipe settings or as repeated `--ignored-windows` arguments; `*` is not a glob. Check exclusions with synthetic windows. Filtering cannot remove sensitive material already captured or guarantee complete exclusion.
• **Raw OCR is not sent to the synthesis backend by this pipeline.** `scripts/fetch_window.py` pulls data over localhost HTTP. `scripts/cluster.py` reduces the timeline to app/duration/title summaries. `scripts/redact.py` scrubs recognizable emails, API keys, bearer tokens, and selected phone formats as defense-in-depth before any cluster summary reaches the LLM.
• **LLM backend defaults to `local`.** Use an already installed chat model served by [LM Studio](https://lmstudio.ai/); its exact server model ID belongs in `local.model`. Summaries stay on the machine only while this endpoint is loopback. A remote HTTPS endpoint also sends summaries off-host. Cloud backends (`claude`, `foundry`) remain opt-in. Detection and embedding inference run locally regardless of backend choice.
• **Dry-run mode** (`--dry-run`) skips skill matching and LLM synthesis and writes a clustered `plan.md`. Review the retained app names and window titles before selecting a cloud backend; the regex scrubber does not guarantee anonymization or removal of unpublished research details.
• **TLS for localhost** (optional, for corporate policy): see `references/https-proxy.md` for the Caddy pattern.

Prerequisites

1. Screenpipe daemon

Either install the official release or build from source. Either way the daemon binds HTTP on `localhost:3030` by default.

**From source** (recommended if you want the CLI daemon without the desktop GUI):

git clone --depth 1 https://github.com/screenpipe/screenpipe.git
cd screenpipe
cargo build -p screenpipe-engine --release
# System deps (macOS): cmake + full Xcode.app (not just Command Line Tools).
#   brew install cmake
#   # if xcodebuild plug-ins error: sudo xcodebuild -runFirstLaunch
./target/release/screenpipe doctor   # confirm permissions + ffmpeg
./target/release/screenpipe record --disable-audio --use-pii-removal \
  --ignored-windows "1Password" --ignored-windows "Bitwarden" \
  --ignored-windows "Private Browsing" --ignored-wi