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.
The knowledge-extractor skill automatically extracts, synthesizes, and preserves organizational knowledge from conversations, debugging sessions, and problem-solving work.
Converts ephemeral session context into persistent knowledge that improves future performance:
Claude, extract what we learned from this debugging session.
Update DISCOVERIES.md with root causes and solutions.
What patterns should we capture from this implementation?
Update PATTERNS.md with reusable approaches.
We've debugged CI failures multiple times with the same process.
Should we create a new agent to automate this?
Perform complete knowledge extraction on this session.
Extract discoveries, patterns, and agent recommendations.
After debugging pre-commit hook failure (45 minutes):
User: Extract what we learned from fixing the pre-commit issue.
Claude:
1. Analyzes the debugging session
2. Identifies root cause: Cloud sync file locks
3. Finds solution: Exponential backoff retry logic
4. Creates DISCOVERIES.md entry with prevention strategy
5. Suggests new pattern for "Resilient File I/O"
Result: Next time pre-commit hook fails, developers reference DISCOVERIES.md and fix in 10 minutes instead of 45.
After implementing module regeneration strategy (multiple sessions):
User: Extract the pattern from this module design approach.
Claude:
1. Analyzes successful implementations
2. Identifies pattern: Brick & studs with __all__ exports
3. Documents reusable approach in PATTERNS.md
4. Includes working examples
5. Notes when/when-not-to-use
Result: Next module automatically uses brick philosophy without rethinking architecture.
After debugging CI failures for the 3rd time (same 5-step process):
User: We keep using the same CI debugging process. Should we automate?
Claude:
1. Recognizes repeated workflow (3+ times)
2. Calculates time savings: 45 min → 15 min per failure
3. Recommends new agent: ci-diagnostic-workflow
4. Provides creation plan with scope and boundaries
Result: New agent automates diagnosis, saves hours per quarter.
What: Problems encountered, root causes, solutions Where: DISCOVERIES.md When to extract: After solving any complex bug or issue Format: Issue → Root Cause → Solution → Learning → Prevention
What: Proven solutions applicable to multiple situations Where: PATTERNS.md When to extract: When solution applies to 2+ different problems Format: Challenge → Solution → Key Points → When to Use
What: Automated workflows for repeated tasks Where: .claude/agents/amplihack/specialized/ When to extract: Workflow repeated 2+ times, saves 30+ minutes Format: Problem → Scope → Process → Value
Review conversation for learnings, patterns, repeated actions
Identify which knowledge type(s) apply
Create structured entries in appropriate locations
Ensure entries meet quality standards
Make knowledge available to agents/systems
| Scenario | Extract To | Example | | ---------------------------- | -------------- | --------------------------------- | | Fixed complex bug | DISCOVERIES.md | "Pre-commit cloud sync issue" | | Repeated workflow 2x | PATTERNS.md | "Resilient file I/O" | | Debugging same issue 3x | New Agent | "ci-diagnostic-workflow" | | New implementation success | PATTERNS.md | "Module regeneration structure" | | Failed approach learned from | DISCOVERIES.md | "Why PBZFT was wrong pattern" | | Environment-specific fix | PATTERNS.md | "Graceful environment adaptation" |
Example: Extracting knowledge from 1-hour debugging session saves ~4 hours over next quarter through prevention + pattern reuse.
"Extract what we learned from solving this problem."
→ Claude automatically identifies discoveries, patterns, agents
→ Updates appropriate knowledge bases
"Extract a pattern for [specific solution]"
→ Claude analyzes and documents pattern
→ Updates PATTERNS.md with working examples
"Perform complete knowledge extraction"
→ Claude extracts all discoveries, patterns, agent opportunities
→ Updates DISCOVERIES.md, PATTERNS.md
→ Recommends new agents if applicable
Every session creates knowledge worth preserving. Without active extraction, that knowledge is lost when conversation ends. Knowledge extraction converts individual learning into organizational capability.
For detailed instructions, see SKILL.md
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Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Start voice calls via the OpenClaw voice-call plugin.
Notion API for creating and managing pages, databases, and blocks.
Gemini CLI for one-shot Q&A, summaries, and generation.
Category:developer