Extracting Knowledge

Announce: "Using kn-extract to extract knowledge."

Core principle: ONLY EXTRACT GENERALIZABLE KNOWLEDGE.

Step 1: Identify Source

json

mcp__knowns__get_task({ "taskId": "$ARGUMENTS" })

Look for: patterns, problems solved, decisions made, lessons learned.

Step 2: Search for Existing Docs

json

mcp__knowns__search_docs({ "query": "<pattern/topic>" })

Don't duplicate. Update existing docs when possible.

Step 3: Create Documentation

json

mcp__knowns__create_doc({
  "title": "Pattern: <Name>",
  "description": "Reusable pattern for <purpose>",
  "tags": ["pattern", "<domain>"],
  "folder": "patterns"
})

mcp__knowns__update_doc({
  "path": "patterns/<name>",
  "content": "# Pattern: <Name>\n\n## Problem\n...\n\n## Solution\n...\n\n## Example\n```typescript\n// Code\n```\n\n## Source\n@task-<id>"
})

Step 4: Create Template (if code-generatable)

json

mcp__knowns__create_template({
  "name": "<pattern-name>",
  "description": "Generate <what>",
  "doc": "patterns/<pattern-name>"
})

Link template in doc:

json

mcp__knowns__update_doc({
  "path": "patterns/<name>",
  "appendContent": "\n\n## Generate\n\nUse @template/<pattern-name>"
})

Step 5: Validate

CRITICAL: After creating doc/template, validate to catch broken refs:

json

mcp__knowns__validate({})

If errors found, fix before continuing.

Step 6: Link Back to Task

json

mcp__knowns__update_task({
  "taskId": "$ARGUMENTS",
  "appendNotes": "📚 Extracted to @doc/patterns/<name>"
})

What to Extract

Source	Extract As	Template?
Code pattern	Pattern doc	✅ Yes
API pattern	Integration guide	✅ Yes
Error solution	Troubleshooting	❌ No
Security approach	Guidelines	❌ No

Checklist

• Knowledge is generalizable
• Includes working example
• Links back to source
• Template created (if applicable)
• Validated (no broken refs)