autoskill

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 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 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; 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-windows "Incognito"

This source-build example is illustrative and was not compiled in the API review. Check the installed screenpipe record --help; permissions and system dependencies vary by platform/release. On macOS, grant the requested Screen Recording/Accessibility permissions and relaunch. Review the full deny-list before real capture.

2. Screenpipe API token

Current Screenpipe enables API auth by default; protected routes such as /search require a bearer token even on loopback. /health is exempt, so a successful health check does not validate search authorization. For an authenticated instance, retrieve its local API token:

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.)

Screenpipe connections permit HTTP only on loopback; remote endpoints require HTTPS. Generated draft names must be valid skill names, so model output cannot write outside the proposal directory.

3. Python environment

Create a separate environment; do not add scientific dependencies to the repository environment:

uv venv .venv-autoskill --python 3.12
uv pip install --python .venv-autoskill/bin/python httpx==0.28.1 pyyaml==6.0.3 sentence-transformers==6.1.0
source .venv-autoskill/bin/activate

The public sentence-transformers/all-MiniLM-L6-v2 model downloads on first use. For an existing cache, set embeddings.local_files_only: true to prevent download attempts. Its 384-dimensional embeddings truncate inputs beyond 256 word pieces; long summaries may lose detail. Similarity is a retrieval heuristic, not proof of workflow equivalence.

4. Local LLM (default path) — LM Studio

• Install LM Studio.
• Choose a chat model already installed on your machine (lms ls). Match the context length to its supported limits and available memory; no particular GPU fit is assumed.
• Load it with a stable identifier and start the API server explicitly:

lms load <installed-model-key> --identifier autoskill-local
lms server start --port 1234
lms server status

This setup is illustrative; no model was downloaded or inferred during this review. lms load does not itself start the HTTP server. If LM Studio's Require Authentication option is enabled (0.4.0+), set LM_API_TOKEN to a token created in its server settings. doctor checks the configured ID against /v1/models; the list can include JIT-loadable models and does not prove inference succeeds.

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, and set foundry.endpoint to https://<resource>.services.ai.azure.com/anthropic (or a gateway with that same Messages contract). Set foundry.model to the deployment name. This adapter supports API-key auth, not Entra token acquisition; Entra-only deployments need a different client.

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 discovered 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:

python skills/autoskill/scripts/autoskill.py doctor --config skills/autoskill/config.yaml --skills-dir skills
python skills/autoskill/scripts/autoskill.py run --start <ISO-start> --end <ISO-end> --config skills/autoskill/config.yaml
python skills/autoskill/scripts/autoskill.py promote --proposed <proposal-dir> --skills-dir skills --name <skill>

0. Preflight with doctor

Before a full run, check connectivity and backend configuration:

python skills/autoskill/scripts/autoskill.py doctor \
  --config skills/autoskill/config.yaml \
  --skills-dir skills

The report covers config (backend choice valid), skills_dir (exists), screenpipe (public health endpoint reachable), and llm (LM Studio lists the configured model, or a cloud API key is present). It does not fetch history, verify Screenpipe search auth, test cloud credentials, run inference, or load embedding weights. Non-zero exit on any failure, with the offending line marked error.

1. Run the pipeline

export SCREENPIPE_TOKEN="$(screenpipe auth token)"
python skills/autoskill/scripts/autoskill.py run \
  --start "2026-04-17T00:00:00Z" \
  --end   "2026-04-17T23:59:59Z" \
  --config skills/autoskill/config.yaml \
  --skills-dir skills

Proposals land in ~/.autoskill/proposed/<timestamp>/ by default, keeping experimental output out of the skills repo. Pass --out PATH to override.

Internally:

1. Fetch — fetch_window uses /search with a fixed time window and limit/offset pagination, explicitly disables cloud results, frame images, and API filter_pii (which can call a remote enclave). It normalizes OCR/UI/accessibility/input/audio rows to {ts, app, window_title, text, content_type}. Memory/parsed records are skipped with a warning because they are not activity events. Malformed or incomplete pagination fails instead of producing a silently partial report.
2. Redact — redact scrubs recognizable secret patterns from event text, app names, and window titles as defense-in-depth over screenpipe's own PII removal.
3. Cluster — segment_sessions splits on idle gaps (default 10 min) and drops short sessions; cluster_sessions groups sessions by the ordered list of distinct apps and keeps clusters of size min_cluster_size (default 2).
4. 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).
5. Synthesize — synthesize prompts the configured LLM backend to classify each cluster as reuse, compose, or novel and emit a SKILL.md body where appropriate.
6. 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:

python skills/autoskill/scripts/autoskill.py promote \
  --proposed ~/.autoskill/proposed/2026-04-17T14-30-00 \
  --skills-dir skills \
  --name zotero-pubmed-helper

Validate each draft with uv run skills-ref validate <draft-directory> and follow the repository's tests/scan rules before promotion. The LLM draft is not automatically spec-validated.

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: local
local:
  endpoint: http://localhost:1234/v1   # LM Studio's Developer server
  model: autoskill-local

screenpipe:
  url: http://localhost:3030           # or https://screenpipe.local via Caddy

cluster:
  min_session_minutes: 5
  idle_gap_minutes: 10
  min_cluster_size: 2

To opt into a cloud backend:

backend: claude                         # or foundry
claude:
  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 pytest suite at tests/autoskill/ in the repository root. Each script is unit-tested in isolation with dependency injection (mock HTTP transport, stub backend, stub embedder):

uv run --with pytest python -m pytest tests/autoskill -q
python tests/run_all.py --isolated autoskill

The 2026-09-30 review used synthetic events, mock HTTP transports, and a local fixture server. No real Screenpipe history, authenticated cloud inference, live LM Studio inference, or embedding-model download was used. See API contract and source review for endpoint details and validation limits.

Composition with other skills in this repo

The autoskill's embedding index discovers sibling SKILL.md files from the configured skills directory at run time. 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.