Vector Memory
This gives you semantic search over stored text without setting up a vector database. It generates embeddings via OpenAI's API and stores them in JSON, then searches using cosine similarity to find memories by meaning rather than exact keywords. Useful if you're building an agent that needs to recall relevant context from past conversations or documents. The trade-off is obvious: you get simplicity and zero infrastructure, but you're limited to in-memory search that won't scale past a few thousand entries. For prototyping or moderate memory needs, though, it's a clean way to add semantic recall without the database overhead.
- skills.sh installs
- 151
- last updated
- Aug 21, 2026
Install
$ npx -y skills add winsorllc/upgraded-carnival --skill vector-memory --agent claude-codeInstalls into .claude/skills of the current project.
Files
Vector Memory Skill
This skill provides vector-based semantic memory storage using embeddings for intelligent recall by meaning.
When to Use
- You need semantic search (find memories by meaning, not keywords)
- You want to retrieve similar documents or conversations
- You're building an agent that needs context-aware memory
- You need to cluster or group related memories
Capabilities
- vstore: Store text with automatic embedding generation
- vsearch: Search memories by semantic similarity
- vdelete: Remove a memory by ID
- vlist: List all stored memories
- vsimilar: Find memories similar to a given ID
- vclear: Clear all memories
How It Works
- Text is converted to embeddings using OpenAI's API
- Embeddings are stored in JSON with metadata
- Search uses cosine similarity to find semantically related memories
- No external vector database required - pure JSON storage
Environment Variables
Required:
OPENAI_API_KEY- For generating embeddings
Optional:
VECTOR_MEMORY_DIM- Embedding dimensions (default: 1536 for text-embedding-ada-002)
Usage Examples
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- Repository
- github.com/winsorllc/upgraded-carnival
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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