memory

Memory Skill Policy

Router Logic

Scenario 1: User wants to store something

1. Analyze: Determine the type of memory (insight, rule, decision)
2. Store: Call save_memory(content, metadata)
3. Confirm: Show the saved memory ID

Scenario 2: User wants to remember/search

1. Search: Call search_memory(query, limit)
2. Format: Present results with relevance scores
3. Respond: "I found X memories about that..."

Scenario 3: User asks "What have you learned?", "Show memories"

1. List: Call get_memory_stats()
2. Recall: Call search_memory() with relevant keywords
3. Present: Show structured summary

Commands Reference

Workflow: Store an Insight

User: Remember that for this project, all commit messages must be in English.

Claude:
  1. save_memory(
       content="All commit messages must be in English only",
       metadata={"domain": "git", "source": "user"}
     )
  2. → Saved memory [a1b2c3d4]: All commit messages must be in English only
  3. → "Got it! I'll remember that commit messages must be in English."

Workflow: Recall Past Learning

User: What do we use for git tags?

Claude:
  1. search_memory("git tags semantic versioning")
  2. → Found 2 matches:
     - [Score: 0.8921] Always use semantic versioning for git tags...
     - [Score: 0.7234] v1.2.3 format for releases
  3. → "I found memories about git tags:
       - Always use semantic versioning for git tags..."

Memory vs Knowledge Skill

Best Practices

1. Store actionable insights, not obvious facts
2. Include domain in metadata for filtering
3. Use clear, searchable phrasing in content
4. Recall before acting on project-specific patterns