autoskill

Extracts durable preferences from coding sessions and suggests targeted Skill updates.

1|Updated Aug 6, 2022
One-click install
npx skills add https://github.com/wormholecowboy/.dotfiles --skill autoskill-wormholecowboy
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: autoskill
Source: https://github.com/wormholecowboy/.dotfiles/tree/main/claude-config/skills/autoskill
Command: npx skills add https://github.com/wormholecowboy/.dotfiles --skill autoskill-wormholecowboy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates extracting durable preferences from coding sessions and proposes targeted updates to the Skills that were active during the session. It helps ensure learning persists across sessions and that edits remain reversible.

Core Features & Use Cases

  • Durable preference extraction: detects repeated corrections and approvals that encode team conventions.
  • Targeted Skill updates: suggests minimal, reversible edits to active Skills for review.
  • Cross-session learning: applies feedback across multiple sessions to improve future Recommendations.

Quick Start

After finishing a coding session, run autoskill to analyze signals and obtain proposed Skill updates to review. Then accept or modify the suggested edits as appropriate.

Frequently Asked Questions about autoskill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract durable preferences from coding sessions to update skills?

Cross-session learning works by analyzing repeated corrections and approvals across multiple coding sessions to detect team conventions, then mapping those signals to relevant active Skills.

How do I start learning from a coding session to improve my skills?

To start learning from a coding session, finish your work and run autoskill; it will analyze the session signals, extract patterns, and suggest minimal Skill updates for you to accept or modify.

Can I apply feedback across multiple coding sessions to improve future recommendations?

Yes, you can apply feedback across multiple coding sessions; autoskill detects repeated corrections and approvals to encode team conventions and improve future Skill recommendations.

What quality filters apply when extracting preferences from coding sessions?

When extracting preferences from coding sessions, a quality filter ensures that suggested Skill updates are repeatable, actionable, and novel before mapping the signals to relevant active Skills.

Are Skill updates generated from session analysis reversible?

Yes, Skill updates generated from session analysis are reversible; autoskill proposes minimal, targeted edits to active Skills so you can safely accept, modify, or reject the suggested changes.