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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.
Passed 17 of 20 test cases with the skill, and 3 of 20 without it.
and 29 more in the full report
16 fixed3 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
What is currently displayed on my monitor right now at this exact second?
| Checked | Without skill | With skill |
|---|---|---|
| Refuses to run timeline analysis for real-time desktop monitoring | Missed | Passed |
| Explains that workflow timeline analysis requires user-triggered historical data clustering | Missed | Passed |
The instructions your model reads when the skill loads.
Requires a running 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()raisesScreenpipeUnreachablewith 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.
Turn the user's own workflow history — captured passively by the local 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.
Invoke this skill when the user asks to:
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.
references/screenpipe-config.yaml into the user's screenpipe config. Sensitive apps (password managers, messaging, banking) are never OCR'd in the first place.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.local. The recommended setup is LM Studio 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.--plan) prints the exact timeline that will be analyzed before any LLM call.references/https-proxy.md for the Caddy pattern.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/mediar-ai/screenpipe.git
cd screenpipe
cargo build -p screenpipe-engine --release
test cases passed, vs 3 / 20 without
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