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Semantic memory retrieval that queries stored learnings from past sessions
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| name | recall |
| description | Semantic memory retrieval that queries stored learnings from past sessions |
| user-invocable | true |
Query the memory system for relevant learnings from past sessions.
/recall <query>
/recall hook development patterns
/recall wizard installation
/recall TypeScript errors
When this skill is invoked:
Determine the memory stack root:
MEMORY_DIR="${MAESTRO_MEMORY_DIR:-$CLAUDE_PROJECT_DIR/opc}"
Check that the recall script exists before running:
if [ ! -f "$MEMORY_DIR/scripts/recall_learnings.py" ]; then
echo "⚠️ Recall unavailable: memory stack not found at $MEMORY_DIR"
echo " Set MAESTRO_MEMORY_DIR to your OPC installation path."
exit 0
fi
cd "$MEMORY_DIR" && PYTHONPATH=. uv run python scripts/recall_learnings.py --query "<ARGS>" --k 5
Where <ARGS> is the query provided by the user.
Present results as:
## Memory Recall: "<query>"
### 1. [TYPE] (confidence: high, id: abc123)
<full content>
### 2. [TYPE] (confidence: medium, id: def456)
<full content>
The user can specify options after the query:
--k N - Return N results (default: 5)--vector-only - Use pure vector search (higher precision)--text-only - Use text search only (faster)Example: /maestro:recall hook patterns --k 10 --vector-only