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.

• "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."

• 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 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.

• Install LM Studio.
• 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):

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

• 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.

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

~/.autoskill/proposed/<timestamp>/

--out PATH

1. Fetch —

   fetch_window

   paginates screenpipe's

   /search

   endpoint, normalizes events to

   {ts, app, window_title, text, content_type}

   .
2. 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.
3. 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).
4. Match —

   load_skill_descriptions

   reads frontmatter from every

   SKILL.md

   in

   academic-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.

--dry-run

plan.md

• 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.