Skill

/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.

One skill from wshobson-agents.

shell

$ npx -y skills add wshobson/agents --skill embedding-strategies --agent claude-code

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Fires itselfClaude auto-loads it when your prompt matches the work.
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Slash command/embedding-strategies

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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.

Stats

Stars38,174

Forks4,092

LanguagePython

LicenseMIT

Ships with wshobson-agents

SKILL.md

embedding-strategies.SKILL.md

---
name: embedding-strategies
description: 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                     |