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:

   bash

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

   Example:

   bash

   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.)