extract

Extract reusable patterns, decisions, and failures into a three-category knowledge document.

Updated Apr 4, 2026
One-click install
npx skills add https://github.com/anionzo/skill --skill extract-anionzo
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: extract
Source: https://github.com/anionzo/skill/tree/main/skills/extract
Command: npx skills add https://github.com/anionzo/skill --skill extract-anionzo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires beads-cli.

What problem does it solve?

Extract reusable patterns, decisions, and failure learnings from completed work into documentation that compounds knowledge over time. This process ensures organizational memory is built from real-world outcomes rather than isolated code changes.

Core Features & Use Cases

  • Durable learning extraction: capture patterns, decisions, and failures from a completed task into a structured knowledge artifact.
  • Session handoff and post-merge compounding: provide compact handoff data for ongoing work and write dated learnings after a merge or abandonment.
  • Dream consolidation: perform on-demand Codex-based consolidation passes over accumulated learnings to improve future work.
  • Knowledge linking: tie learnings back to source tasks and artifacts for traceability.

Quick Start

Process a completed feature to generate a durable learnings document and a compact handoff payload.

Frequently Asked Questions about extract

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

FAQPage Schema
How do I extract reusable patterns and decisions from completed software tasks?

You document reusable learnings by processing completed software tasks to identify patterns, decisions, and failures, producing a structured knowledge artifact. This captures real-world outcomes into durable documentation that compounds organizational memory over time.

What is the best way to document post-merge learnings for long-term knowledge retention?

The best way to document post-merge learnings is capturing patterns, decisions, and failures into a three-category knowledge document. These artifacts are stored under history/learnings with source task references, ensuring long-term knowledge retention and traceability.

Can I generate a handoff payload for ongoing debugging sessions?

Yes, you can generate a compact handoff payload for ongoing debugging sessions and feature handoffs. This provides structured handoff data where long-term learnings are captured and organized for continuity.

Does this knowledge extraction process require beads-cli to run?

Yes, this knowledge extraction process requires beads-cli as a dependency to run. It uses the CLI to process completed work and generate durable knowledge artifacts stored under the learnings directory.

When should I consolidate accumulated learnings to improve future work?

You should consolidate accumulated learnings when you want to improve future work through on-demand passes over your knowledge base. This performs consolidation passes over captured patterns and failures to refine organizational memory.

How are extracted knowledge artifacts linked back to source code and tasks?

Extracted knowledge artifacts are linked back to source code and tasks through explicit knowledge tying. The generated durable documents include references to source tasks and artifacts for full traceability of decisions and failures.