Recall Memory

This skill allows you to search your memory system using semantic queries.

Workflow

1. Formulate Your Query: Think about what you're trying to find:

   • A solution to a specific problem (e.g., "How do I fix CORS errors?")
   • A pattern or best practice (e.g., "Python async patterns")
   • Historical context (e.g., "What did we decide about routing?")

2. Run the Search: Execute the memory manager recall command:

   uv run python .fleet/context/scripts/memory_manager.py recall "<your query>"
   

   Example:

   uv run python .fleet/context/scripts/memory_manager.py recall "memory system implementation"
   

3. Review Results: The system will return:

   • Top matches from semantic memory (facts, decisions)
   • Relevant skills from procedural memory (how-tos)
   • Similarity scores to gauge relevance
   • Source metadata (file paths, timestamps)

4. Refine if Needed: If results aren't relevant, try:

   • More specific queries (add context/domain)
   • Different terminology (synonyms)
   • Breaking complex queries into simpler parts

Tips

• Use natural language - the system uses semantic search, not keyword matching
• Be specific - "fix DSPy routing errors" is better than "errors"
• Combine with other commands: recall → apply solution → learn new variation
• Check episodic memory separately if you need conversation history

Output Format

Results include:

• Matched text snippets
• Source file paths
• Relevance scores (0-1, higher = better match)
• Metadata (creation date, tags, etc.)