When should I trigger this skill?

It is best used at the end of a coding session, after resolving a complex bug, or when you realize you've performed a specific workflow multiple times and want to formalize it.

Does it work with existing project documentation?

Absolutely. It follows specific templates for DISCOVERIES.md and PATTERNS.md to ensure your technical documentation remains structured, searchable, and useful for the entire team.

How does Knowledge Extractor save information?

It uses the Kuzu memory backend via the amplihack framework to store discoveries and updates local files like PATTERNS.md or DISCOVERIES.md for human-readable reference.

Can it help create new agents?

Yes, the skill identifies repeated workflows and suggests when a task is frequent enough to justify creating a new specialized agent in your .claude/agents directory.

What is the primary benefit of using this skill?

It prevents 'knowledge rot' by converting temporary chat context into long-term assets, making both the human developer and the AI agent more efficient in future sessions.

Knowledge Extractor

Name: Knowledge Extractor
Author: rysweet

byrysweet

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Productivity & Workflow

Automatically captures and preserves insights from debugging sessions and problem-solving to build persistent organizational memory.

About

The Knowledge Extractor skill transforms ephemeral coding sessions into persistent organizational intelligence. By analyzing conversations, debugging history, and failed attempts, it identifies root causes, reusable patterns, and automation opportunities. The skill integrates with memory backends like Kuzu to store discoveries and suggests updates to local documentation, ensuring that hard-won technical insights are preserved for future use. This prevents the repetition of past mistakes and helps streamline the development process by recommending when to create specialized coding agents for repeated workflows.

Key Features

Automated session analysis for key learning extraction
Intelligent recommendations for new specialized agent creation
Identification and formalization of reusable architectural patterns
17 GitHub stars
Standardized documentation for discoveries and root causes
Persistent memory storage via Kuzu backend integration

Use Cases

Extracting reusable architectural patterns from successful feature implementations
Documenting root causes after resolving complex, time-consuming bugs
Creating post-mortem analysis for failed development approaches to avoid future pitfalls

About

Key Features

Automated session analysis for key learning extraction
Intelligent recommendations for new specialized agent creation
Identification and formalization of reusable architectural patterns
17 GitHub stars
Standardized documentation for discoveries and root causes
Persistent memory storage via Kuzu backend integration

Use Cases

Extracting reusable architectural patterns from successful feature implementations
Documenting root causes after resolving complex, time-consuming bugs
Creating post-mortem analysis for failed development approaches to avoid future pitfalls