qredence/agentic-fleet/recall

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?")

1. 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" ``

1. 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)

1. 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.)