Extracts key learnings from conversations, debugging sessions, and failed attempts. Use at session end or after solving complex problems to capture insights. Stores discoveries in memory (via amplihack.memory.discoveries), suggests PATTERNS.md updates, and recommends new agent creation. Ensures knowledge persists across sessions via Kuzu memory backend.
View on GitHub.claude/skills/knowledge-extractor/SKILL.md
January 21, 2026
Select agents to install to:
npx add-skill https://github.com/rysweet/amplihack/blob/main/.claude/skills/knowledge-extractor/SKILL.md -a claude-code --skill knowledge-extractorInstallation paths:
.claude/skills/knowledge-extractor/# Knowledge Extractor Skill ## Purpose This skill automatically extracts, synthesizes, and preserves knowledge from conversations, debugging sessions, failed attempts, and solved problems. It converts ephemeral interactions into persistent organizational knowledge that improves future performance. ## When to Use This Skill - **Session End Analysis**: Extract learnings before session context is lost - **After Complex Debugging**: Capture root causes and solutions while fresh - **Following Failed Attempts**: Document what didn't work and why - **Successful Problem Solving**: Preserve solutions for future reuse - **New Pattern Discovery**: Identify patterns that should be documented - **Repeated Workflows**: Recognize when to create new specialized agents - **Cross-Session Learning**: Build organizational memory from individual sessions ## Core Philosophy: Knowledge Preservation **Session Context**: Ephemeral conversation context that will be lost without active preservation **Persistent Knowledge**: Structured learnings that improve future performance **Pattern Recognition**: Identifying when solutions are repeated and should be automated **Organizational Growth**: Converting individual learning into system-wide improvement ## Knowledge Extraction Framework ### Three Types of Knowledge Extraction #### 1. Discoveries - Novel Insights and Root Causes **What it captures**: Problems encountered, root causes identified, solutions implemented **When to extract**: - After solving a complex bug - When debugging reveals unexpected behavior - When discovering wrong assumptions - After identifying missing functionality - When learning why something failed **Format for DISCOVERIES.md**: ```markdown ## [Brief Title] (YYYY-MM-DD) ### Issue What problem or challenge was encountered? ### Root Cause Why did this happen? What was the underlying issue? ### Solution How was it resolved? Include code examples if relevant. ### Key Learnings What insights were gained?