Recall Memory
This skill allows you to search your memory system using semantic queries.
Workflow
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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?")
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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" -
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)
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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.)