recall
Query the memory system for relevant learnings from past sessions using semantic search.
Best use case
recall is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Query the memory system for relevant learnings from past sessions using semantic search.
Teams using recall should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/recall/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How recall Compares
| Feature / Agent | recall | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Query the memory system for relevant learnings from past sessions using semantic search.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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`
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