Agent skill · Code Review & Quality

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.

Seth Hobson38,331★ · +219/wk · 1 repos on radarProfile →
claude-codecodexcopilotcursorMIT
Install
npx skills add wshobson/agents --skill embedding-strategies --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 3 KB
Bundled scripts: none
Path: plugins/llm-application-dev/skills/embedding-strategies/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 38,479 · +148 this week
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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

What's inside
Steps it walks through
  1. When to Use This Skill
  2. Core Concepts
  3. 1. Embedding Model Comparison (2026)
  4. 2. Embedding Pipeline
  5. Templates and detailed worked examples
  6. Best Practices
  7. Do's
  8. Don'ts
Ships with 1 file
  • references/details.md
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About this skill
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 wshobson/agents --skill embedding-strategies --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 wshobson/agents, a repository with 38,479 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.

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