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.
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"}]
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() 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 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 filters apps/windows at capture time. Merge references/screenpipe-config.toml into ~/.screenpipe/config.toml — the CLI parses that path as TOML — or pass the same entries as repeated --ignored-windows flags. Entries are case-insensitive substrings, not globs. Confirm the deny-list works against a running daemon before relying on it; until then treat capture-time exclusion as unverified and scripts/redact.py as the enforced layer.
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 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):
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:
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:
pipenv install httpx pyyaml sentence-transformers
The embedding model (sentence-transformers/all-MiniLM-L6-v2, ~80 MB) downloads on first run.
Before a full run, verify every dependency in one shot:
python scripts/autoskill.py doctor \
--config skills/autoskill/config.yaml \
--skills-dir skills
The report covers config (backend choice valid), skills_dir (exists), screenpipe (reachable + authed), and llm (LM Studio serving or API key present). Non-zero exit on any failure, with the offending line marked error.
Redact — redact scrubs emails, API keys, bearer tokens, phones from OCR text and window titles as defense-in-depth over screenpipe's own PII removal.
Cluster — segment_sessions splits on idle gaps (default 10 min) and drops short sessions; cluster_sessions groups sessions by app-signature and keeps clusters of size min_cluster_size (default 2).
Match — load_skill_descriptions reads frontmatter from every SKILL.md in skills/; top_k_matches ranks each cluster against all skills using local sentence-transformers embeddings (cosine similarity).
Synthesize — synthesize prompts the configured LLM backend to classify each cluster as reuse, compose, or novel and emit a SKILL.md body where appropriate.
Report — writes <out_dir>/<ts>/report.md, plus new-skills/<name>/SKILL.md or composition-recipes/<name>/SKILL.md for each proposal.
Add --dry-run to stop after clustering; this skips the LLM (and the sentence-transformers load), writing only plan.md for inspection.
2. Review and promote
Open ~/.autoskill/proposed/<ts>/report.md, edit drafts in place, delete anything you don't want. Then:
promote moves the directory into skills/<name>/, refusing to overwrite an existing skill. Exits non-zero with a friendly error if the proposal isn't found or the target already exists.
Configuration
See config.yaml for the full shape. Default values (local-first):
backend:locallocal:endpoint:http://localhost:1234/v1# LM Studio's Developer servermodel:Gemma-4-31B-itscreenpipe:url:http://localhost:3030# or https://screenpipe.local via Caddycluster:min_session_minutes:5idle_gap_minutes:10min_cluster_size:2
To opt into a cloud backend:
backend:claude# or foundryclaude:model:claude-opus-4-7
Composition recipes vs new skills
compose: the LLM judged that chaining existing skills covers the workflow. The emitted SKILL.md is intentionally thin — frontmatter + a "Workflow" section that invokes existing skills in order. The same agent runtime that discovered the skill can then invoke it end-to-end.
novel: no combination of existing skills covers it. A fuller SKILL.md is drafted, still following repo conventions (frontmatter, Overview, When to Use, Workflow). The user should always review new-skill drafts before promoting.
Testing
The skill is covered by a small pytest suite at tests/. Each script is unit-tested in isolation with dependency injection (mock HTTP transport, stub backend, stub embedder):
cd skills/autoskill
python -m pytest tests/ -v
Composition with other skills in this repo
The autoskill's embedding index covers all 135 sibling skills. Workflows that look like scientific writing will match scientific-writing / literature-review / citation-management; figure work will match scientific-schematics / generate-image / infographics; slide prep matches scientific-slides / pptx; etc. When a cluster scores high against two or three sibling skills the emitted composition recipe names them explicitly, so the user's future agent invocations use the optimized paths already documented in this repo.
