Recall
- 476 installs
- 3.9k repo stars
- Updated January 26, 2026
- parcadei/continuous-claude-v3
recall is a semantic memory retrieval skill that searches PostgreSQL BGE embeddings for past session learnings for developers who need relevant prior fixes and patterns during agent sessions.
About
recall is a non-user-invocable memory skill in continuous-claude-v3 that runs semantic search against stored learnings and returns the top 5 results with full content, learning type, confidence, and session context. Invocation uses /recall <query> and executes recall_learnings via uv run python under $CLAUDE_OPC_DIR with PYTHONPATH set. Example queries include hook development patterns, wizard installation, and TypeScript errors. A developer reaches for recall when an agent should pull prior debugging notes, install steps, or architectural decisions from earlier Claude sessions instead of starting from zero.
- recall
Recall by the numbers
- 476 all-time installs (skills.sh)
- +2 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #855 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 476 |
|---|---|
| repo stars | ★ 3.9k |
| Last updated | January 26, 2026 |
| Repository | parcadei/continuous-claude-v3 ↗ |
How do agents retrieve past session learnings semantically?
Use recall for development tasks
Who is it for?
Developers running continuous-claude-v3 who want agents to reuse prior session fixes, hook patterns, and install knowledge via semantic search.
Skip if: Teams without the continuous-claude PostgreSQL memory store or who only need live codebase search should skip recall.
When should I use this skill?
An agent needs prior session learnings about hooks, installs, TypeScript errors, or repeated debugging topics before taking action.
What you get
Top 5 learning records with content, learning type, confidence score, and originating session context.
- ranked learning results
- confidence metadata
- session context snippets
By the numbers
- Returns top 5 semantic search results per recall query
Files
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 errorsWhat 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:
cd $CLAUDE_OPC_DIR && PYTHONPATH=. uv run python scripts/core/recall_learnings.py --query "<ARGS>" --k 5Where <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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How many results does recall return?
recall returns the top 5 semantic matches with full content plus learning type, confidence, and session context for each stored learning.
What storage backs recall search?
recall runs semantic search against stored learnings in PostgreSQL using BGE embeddings, executed via uv run python scripts under $CLAUDE_OPC_DIR.
How do you invoke recall?
recall uses /recall <query> examples such as /recall hook development patterns or /recall TypeScript errors to fetch relevant prior learnings.