embeddings
Text embeddings for semantic search and similarity. Use when converting text to vectors, choosing embedding models, implementing chunking strategies, or building document similarity features.
npx skills add majiayu000/claude-skill-registry --skill embeddings --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.
# Embeddings Convert text to dense vector representations for semantic search and similarity. ## Quick Reference ```python from openai import OpenAI client = OpenAI() # Single text embedding response = client.embeddings.create( model="text-embedding-3-small", input="Your text here" ) vector = response.data[0].embedding # 1536 dimensions ``` ```python # Batch embedding (efficient) texts = ["text1", "text2", "text3"] response = client.embeddings.create( model="text-embedding-3-small", input=texts ) vectors = [item.embedding for item in response.data] ``` ## Model Selection | Model | Dims | Cost | Use Case | |-------|------|------|----------| | `text-embedding-3-small` | 1536 | $0.02/1M | General purpose | | `text-embedding-3-large` | 3072 | $0.13/1M | High accuracy | | `nomic-embed-text` (Ollama) | 768 | Free | Local/CI | ## Chunking Strategy ```python def chunk_text(text: str, chunk_size: int = 512, overlap: int = 50) -> list[str]: """Split text into overlapping chunks for embedding.""" words = text.split() chunks = [] for i in range(0, len(words), chunk_size - overlap): chunk = " ".join(words[i:i + chunk_size]) if chunk: chunks.append(chunk) return chunks ``` **Guidelines:** - Chun
- Quick Reference
- Model Selection
- Chunking Strategy
- Similarity Calculation
- Key Decisions
- Common Mistakes
- Advanced Patterns
- Related Skills
- Capability Details
- text-to-vector
- semantic-search
- chunking-strategies
- batch-embedding
- local-embeddings
What does the embeddings skill do?
Text embeddings for semantic search and similarity. Use when converting text to vectors, choosing embedding models, implementing chunking strategies, or building document similarity features.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill embeddings --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 majiayu000/claude-skill-registry, a repository with 534 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.