recall
Query the memory system for relevant learnings from past sessions
Profile →npx skills add parcadei/Continuous-Claude-v3 --skill recall --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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`
- Usage
- Examples
- What It Does
- Execution
- Output Format
- Options
cd $CLAUDE_OPC_DIR && PYTHONPATH=. uv run python scripts/core/recall_learnings.py --query "<ARGS>" --k 5
What does the recall skill do?
Query the memory system for relevant learnings from past sessions
How do I install it?
Run `npx skills add parcadei/Continuous-Claude-v3 --skill recall --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From parcadei/Continuous-Claude-v3, a repository with 3,879 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.