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Autoskill: Propose Skills From Your Work History

Analyzes your recent screen-history windows to spot repeated workflows and draft new skill proposals.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
15 files

What it does

When you ask, this skill reads your recent activity log from the local Screenpipe app (a tool that passively records which apps and window titles you use), groups repeated workflows into clusters, and checks them against the scientific skills already in this repository. It then writes staged draft skills or composition recipes into a folder for you to review, edit, and promote; nothing is installed automatically. It is designed to run only when you explicitly ask.

Great for

  • Researchers who repeat similar analysis workflows and want help turning them into reusable skills
  • Developers maintaining a skills library who want suggestions grounded in real usage patterns
  • Users who already run Screenpipe locally and understand what it records

Skip it if

  • People who have not set up Screenpipe and reviewed its capture exclusions: it reads sensitive screen content, and filtering cannot guarantee that passwords or unpublished research are removed
  • Anyone wanting real-time screen queries or general questions about Screenpipe, since the skill is built for on-demand history analysis only
  • Sensitive or confidential environments where window titles could be sent to a cloud model; the redaction step is only a best-effort scrubber

Try asking

  • “Analyze my last 4 hours and propose new skills for the workflows I repeated.”
  • “Look at what I've been doing this week and tell me what's not covered by existing skills.”
  • “Draft a skill from my recent data-cleaning workflow.”
  • “Find composition recipes for the research workflows I repeat.”
Step-by-step guide: how to use Autoskill: Propose Skills From Your Work HistoryInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 15 original files, the MIT licence and an attribution note.

    autoskill.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip autoskill.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip autoskill.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Set Screenpipe's ignored-window list (password managers, private browsing) before collecting any history, and test exclusions with synthetic windows.
  • Start with --dry-run to review the clustered plan.md and the app names and window titles it retains before choosing any cloud backend.
  • Keep the LLM backend on local LM Studio unless you deliberately accept that redacted cluster summaries will leave your machine.

What's inside

  • config.yaml
  • references/api-contracts.md
  • references/https-proxy.md
  • references/screenpipe-config.yaml
  • scripts/autoskill.py
  • scripts/backends.py
  • scripts/cluster.py
  • scripts/doctor.py
  • scripts/fetch_window.py
  • scripts/match_skills.py
  • scripts/promote.py
  • scripts/redact.py
  • scripts/run.py
  • scripts/synthesize.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.