embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
npx skills add majiayu000/claude-skill-registry --skill embedding-strategies-ccf-claude-code-ccf-mark --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.
# Embedding Strategies Guide to selecting and optimizing embedding models for vector search applications. ## When to Use This Skill - Choosing embedding models for RAG - Optimizing chunking strategies - Fine-tuning embeddings for domains - Comparing embedding model performance - Reducing embedding dimensions - Handling multilingual content ## Core Concepts ### 1. Embedding Model Comparison | Model | Dimensions | Max Tokens | Best For | | -------------------------- | ---------- | ---------- | ----------------- | | **text-embedding-3-large** | 3072 | 8191 | High accuracy | | **text-embedding-3-small** | 1536 | 8191 | Cost-effective | | **voyage-2** | 1024 | 4000 | Code, legal | | **bge-large-en-v1.5** | 1024 | 512 | Open source | | **all-MiniLM-L6-v2** | 384 | 256 | Fast, lightweight | | **multilingual-e5-large** | 1024 | 512 | Multi-language | ### 2. Embedding Pipeline ``` Document → Chunking → Preprocessing → Embedding Model → Vector ↓ [Overlap, Size] [Clean, Normalize] [API/Local] ``` ## Templates ### Template 1: OpenAI Embeddings ```python from openai import OpenAI from typing import List import numpy as np client = OpenAI() def get_embeddings( texts: List[str], model: str = "tex
- When to Use This Skill
- Core Concepts
- 1. Embedding Model Comparison
- 2. Embedding Pipeline
- Templates
- Template 1: OpenAI Embeddings
- Template 2: Local Embeddings with Sentence Transformers
- Template 3: Chunking Strategies
- Template 4: Domain-Specific Embedding Pipeline
- Template 5: Embedding Quality Evaluation
- Best Practices
- Do's
- Don'ts
- Resources
What does the embedding-strategies skill do?
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill embedding-strategies-ccf-claude-code-ccf-mark --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.