autoskill

Files (28)

• .gitignore
• SKILL.md
• config.yaml
• https-proxy.md · references/
• screenpipe-config.yaml · references/
• autoskill.py · scripts/
• backends.py · scripts/
• cluster.py · scripts/
• doctor.py · scripts/
• fetch_window.py · scripts/
• match_skills.py · scripts/
• promote.py · scripts/
• redact.py · scripts/
• run.py · scripts/
• synthesize.py · scripts/
• conftest.py · tests/
• smoke_lmstudio.py · tests/
• test_backends.py · tests/
• test_cli.py · tests/
• test_cluster.py · tests/
• test_doctor.py · tests/
• test_e2e.py · tests/
• test_fetch_window.py · tests/
• test_match_skills.py · tests/
• test_promote.py · tests/
• test_redact.py · tests/
• test_run.py · tests/
• test_synthesize.py · tests/

SKILL.md (first lines)

# 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."
- "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

- **Screenpipe handles app/window filtering at capture time.** Install a starter deny-list by copying `references/screenpipe-config.yaml` into the user's screenpipe config. Sensitive apps (password managers, messaging, banking) are never OCR'd in the first place.
- **Raw OCR never leaves the machine.** `scripts/fetch_window.py` pulls data over localhost HTTP. `scripts/cluster.py` reduces the timeline to app/duration/title summaries. `scripts/redact.py` strips emails, API keys, bearer tokens, and phone numbers as defense-in-depth before any cluster summary reaches the LLM.
- **LLM backend defaults to `local`.** The recommended setup is [LM Studio](https://lmstudio.ai/) running `Gemma-4-31B-it` — strong reasoning at a size that fits on most workstation GPUs, and no data ever leaves your machine. Cloud backends (`claude`, `foundry`) are opt-in and documented in `config.yaml` for users who explicitly want them. Detection and embeddings always run locally regardless of backend choice.
- **Dry-run mode** (`--plan`) prints the exact timeline that will be analyzed before any LLM call.
- **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):

```bash
git clone --depth 1 https://github.com/mediar-ai/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
```

First run will prompt for macOS Screen Recording permission. Grant it and relaunch.

### 2. Screenpipe API token

The local API now requires bearer auth. Retrieve your token and export it:

```bash
export SCREENPIPE_TOKEN=$(screenpipe auth token)
```

(Or set `screenpipe.token` directly in `config.yaml` — env var is preferred since it keeps secrets out of version control.)

### 3. Python environment

Via `pipenv` from the repo root:

```bash
pipenv install httpx pyyaml sentence-transformers
```

The embedding model (`sentence-transformers/all-MiniLM-L6-v2`, ~80 MB) downloads on first run.

### 4. Local LLM (default path) — LM Studio

- Install [LM Studio](https://lmstudio.ai/).
- Download `Gemma-4-31B-it` (or another strong reasoning model; adjust `local.model` in `config.yaml`).
- Load it via the CLI for headless use (no GUI required):

```bash
lms load gemma-4-31b-it --context-length 131072 --gpu max -y
lms status   # confirm server running on :1234
```

### 5. Cloud LLM backends (optional, opt-in)

Only if you explicitly opt out of local:
- `claude`: set `ANTHROPIC_API_KEY`, flip `backend: claude` in `config.yaml`.
- `foundry`: set `FOUNDRY_API_KEY`, flip `backend: foundry`, set `foundry.endpoint` to your corporate gateway URL.

## Architecture

```
screenpipe daemon (user-installed)
        │  HTTP on localhost:3030
        ▼
scripts/fetch_window.py    → normalized timeline events
scripts/redact.py          → regex scrub (defense-in-depth)
scripts/cluster.py         → sessions + clusters (local only)
scripts/match_skills.py    → top-k vs existing 135 skills (local embeddings)
scripts/synthesize.py      → LLM judge: reuse / compose / novel
        │
        ▼
~/.autoskill/proposed/<timestamp>/        (default; override with --out)
  ├── report.md
  ├── composition-recipes/<name>/SKILL.md
  └── new-skills/<name>/SKILL.md

scripts/promote.py         → user-approved proposal → skills/<name>/
```

## Workflow

The skill ships a unified CLI at `scripts/autoskill.py` with three subcommands:

```bash
python scripts/autoskill.py doctor   --config config.yaml --skills-dir ../
python scripts/autoskill.py run      --start ... --end ... --config config.yaml
python scripts/autoskill.py promote  --proposed ~/.autoskill/proposed/<ts> --skills-dir ../ --name <skill>
```

### 0. Preflight with `doctor`

From tassiovale/claude-code-kit, MIT.

More from tassiovale/claude-code-kit