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---
name: recall
description: Query the memory system for relevant learnings from past sessions using semantic search.
---
# Recall - Semantic Memory Retrieval
Query the memory system for relevant learnings from past sessions.
## Usage
```
/recall <query>
```
## Examples
```
/recall hook development patterns
/recall wizard installation
/recall TypeScript errors
```
## What It Does
1. Runs semantic search against stored learnings (PostgreSQL + BGE embeddings)
2. Returns top 5 results with full content
3. Shows learning type, confidence, and session context
## Execution
When this skill is invoked, run:
```bash
cd $CLAUDE_OPC_DIR && PYTHONPATH=. uv run python scripts/core/recall_learnings.py --query "<ARGS>" --k 5
```
Where `<ARGS>` is the query provided by the user.
## Output Format
Present results as:
```
## Memory Recall: "<query>"
### 1. [TYPE] (confidence: high, id: abc123)
<full content>
### 2. [TYPE] (confidence: medium, id: def456)
<full content>
```
## Options
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: `/recall hook patterns --k 10 --vector-only`
