autoskill
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 th
npx skills add K-Dense-AI/scientific-agent-skills --skill autoskill --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 r
- Overview
- When to Use This Skill
- Privacy Posture
- Prerequisites
- 1. Screenpipe daemon
- 2. Screenpipe API token
- 3. Python environment
- 4. Local LLM (default path) — LM Studio
- 5. Cloud LLM backends (optional, opt-in)
- Architecture
- Workflow
- 0. Preflight with doctor
- 1. Run the pipeline
- 2. Review and promote
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 export SCREENPIPE_TOKEN=$(screenpipe auth token) pipenv install httpx pyyaml sentence-transformers lms load gemma-4-31b-it --context-length 131072 --gpu max -y lms status # confirm server running on :1234 python scripts/autoskill.py doctor --config config.yaml --skills-dir ../
What does the autoskill skill do?
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 th
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill autoskill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.