embedding-strategies
🎯SkillHelps you choose and tune embedding models, which turn text into numbers so an AI search can find content by meaning. It covers how to split documents and which model fits code, legal, financial or multilingual text.
Installation
npx skills add wshobson/agents --skill embedding-strategiesRun it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).
Uses skills.sh · Node.js 22.20+
After installing
Ask for related work and the agent uses it on its own, or call it yourself.
/embedding-strategiesInstall by hand
Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.
Open the skill folder →- This project
.claude/skills/embedding-strategies/ - All projects
~/.claude/skills/embedding-strategies/
These paths are for Claude Code. For other tools, the command above puts it in the right place.
What this skill does
Overview
Embeddings are lists of numbers that capture the meaning of text. Search tools and RAG apps, which let an AI answer from your own documents, depend on them. This skill guides you in choosing a model and preparing your text well.
Key Features
- Compare popular models side by side by size, length limit and best use.
- Pick a model that fits code, legal, financial or multilingual content.
- Plan chunking, which means splitting documents into pieces that keep their meaning.
- Save time and cost by sending requests in batches and storing results for reuse.
- Avoid common mistakes like mixing models or going past length limits.
Who is this for?
It is for developers building search or document question-and-answer apps. It notes that Voyage AI models are recommended by Anthropic for Claude apps. Detailed templates and worked examples sit in references/details.md.
Summarized by AI from this skill's SKILL.md. Suggest a better one
SKILL.md
View on GitHub →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.
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 (2026)
| Model | Dimensions | Max Tokens | Best For |
|---|---|---|---|
| voyage-3-large | 1024 | 32000 | Claude apps (Anthropic recommended) |
| voyage-3 | 1024 | 32000 | Claude apps, cost-effective |
| voyage-code-3 | 1024 | 32000 | Code search |
| voyage-finance-2 | 1024 | 32000 | Financial documents |
| voyage-law-2 | 1024 | 32000 | Legal documents |
| text-embedding-3-large | 3072 | 8191 | OpenAI apps, high accuracy |
| text-embedding-3-small | 1536 | 8191 | OpenAI apps, cost-effective |
| bge-large-en-v1.5 | 1024 | 512 | Open source, local deployment |
| 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 and detailed worked examples
Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.
Best Practices
Do's
- Match model to use case: Code vs prose vs multilingual
- Chunk thoughtfully: Preserve semantic boundaries
- Normalize embeddings: For cosine similarity search
- Batch requests: More efficient than one-by-one
- Cache embeddings: Avoid recomputing for static content
- Use Voyage AI for Claude apps: Recommended by Anthropic
Don'ts
- Don't ignore token limits: Truncation loses information
- Don't mix embedding models: Incompatible vector spaces
- Don't skip preprocessing: Garbage in, garbage out
- Don't over-chunk: Lose important context
- Don't forget metadata: Essential for filtering and debugging
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