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Agent skill

Embedding Strategies

by wshobson in wshobson/agents

Helps choose and tune embedding models for semantic search and RAG: model comparison, chunking, preprocessing, normalization and caching.

MITAuto-check passedAI & LLM Engineering

Install Embedding Strategies

skills CLI
$ npx skills add wshobson/agents --skill embedding-strategies -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wshobson/agents embedding-strategies --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wshobson/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/llm-application-dev/skills/embedding-strategies .claude/skills/embedding-strategies && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
embedding-strategies
GitHub stars
40k
Used in
10 other repos
Token cost
~710 tokens
SKILL.md length
218 words
Files
2 (incl. references)
Skills in repo
142
Repo updated
First seen
Licence
MIT

At a glance

Helps choose and tune embedding models for semantic search and RAG: model comparison, chunking, preprocessing, normalization and caching.

  • Works in 2 steps: Embedding Model Comparison (2026) → Embedding Pipeline
  • Choosing an embedding model for a RAG application
  • SKILL.md covers When to Use This Skill, Core Concepts, Templates and detailed worked… and Best Practices
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The skill compares ten embedding models by dimensions, maximum token length and best use. They include Voyage models for general, code, finance and legal text, OpenAI's text-embedding-3 pair, open-source options such as bge-large and multilingual-e5-large for local or multi-language work, and a small fast MiniLM model. It frames the pipeline as chunking, preprocessing, embedding and storing the vector.

Do's include matching the model to the use case, chunking along semantic boundaries, normalizing vectors for cosine similarity, batching requests and caching embeddings for static content. Don'ts include ignoring token limits, mixing models in one index, whose vector spaces are incompatible, and skipping preprocessing. The skill recommends Voyage AI for Claude applications. Templates are in `references/details.md`, and the excerpt is cut off in the don'ts list.

When your agent uses it

  • Choosing an embedding model for a RAG application
  • Tuning chunk size and overlap for better retrieval
  • Picking a model for code, legal, finance or multilingual content
  • Reducing embedding dimensions or caching embeddings to save cost

Example prompts

  • “Which embedding model should I use for searching a large legal document archive?”
  • “Review my chunking and embedding pipeline and suggest changes to improve retrieval quality.”
  • “Compare an open-source embedding model with OpenAI's text-embedding-3-small for our support articles.”

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Embedding Model Comparison (2026)
  2. Embedding Pipeline

What it can do on your machine

Read from SKILL.md and the folder at commit 46891e7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Embedding Strategies loads about 710 tokens when it runs, and up to ~4.9k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 218 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~710
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wshobson/agents at commit 46891e7, republished under its MIT licence (© wshobson). 218 words, ~710 tokens.

Download SKILL.mdSave it as .claude/skills/embedding-strategies/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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)
ModelDimensionsMax TokensBest For
voyage-3-large102432000Claude apps (Anthropic recommended)
voyage-3102432000Claude apps, cost-effective
voyage-code-3102432000Code search
voyage-finance-2102432000Financial documents
voyage-law-2102432000Legal documents
text-embedding-3-large30728191OpenAI apps, high accuracy
text-embedding-3-small15368191OpenAI apps, cost-effective
bge-large-en-v1.51024512Open source, local deployment
all-MiniLM-L6-v2384256Fast, lightweight
multilingual-e5-large1024512Multi-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

© wshobson, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/llm-application-dev/skills/embedding-strategies of wshobson/agents.

  • SKILL.md
  • references/details.md

Open the folder on GitHubat commit 46891e7

Used in 10 other repositories

We found 24 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 10 other GitHub owners. This page covers the copy in wshobson/agents, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Embedding Strategies next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Embedding Strategies compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Embedding Strategies this skillwshobson/agents40k10 repos~710Automated safety check: PassMIT
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k7 repos~2.3kAutomated safety check: PassMIT
RAG ArchitectJeffallan/claude-skills12k—~2kAutomated safety check: PassMIT
Embeddings via 9Routerdecolua/9router31k—~604Automated safety check: PassMIT
Qianwenai Wikichujianyun/skills742—~717Automated safety check: PassCustom licence
Langchain RAGlangchain-ai/langchain-skills1.3k—~3.9kAutomated safety check: PassMIT

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Works with

Categories

Questions about Embedding Strategies

What does Embedding Strategies do?

Helps choose and tune embedding models for semantic search and RAG: model comparison, chunking, preprocessing, normalization and caching. The skill compares ten embedding models by dimensions, maximum token length and best use. They include Voyage models for general, code, finance and legal text, OpenAI's text-embedding-3 pair, open-source options such as bge-large and multilingual-e5-large for local or multi-language work, and a small fast MiniLM model.

When should I use Embedding Strategies?

Embedding Strategies fits situations like: choosing an embedding model for a RAG application; tuning chunk size and overlap for better retrieval; picking a model for code, legal, finance or multilingual content; reducing embedding dimensions or caching embeddings to save cost.

How do I install Embedding Strategies in Claude Code?

Run `npx skills add wshobson/agents --skill embedding-strategies -a claude-code`. Or copy the skill folder (plugins/llm-application-dev/skills/embedding-strategies in wshobson/agents) into .claude/skills/embedding-strategies in your project. Claude Code loads it when a task matches its description.

How do I install Embedding Strategies in Codex?

Run `npx skills add wshobson/agents --skill embedding-strategies -a codex`. Or copy the skill folder (plugins/llm-application-dev/skills/embedding-strategies in wshobson/agents) into .agents/skills/embedding-strategies in your project. Codex loads it when a task matches its description.

Can I use Embedding Strategies in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wshobson/agents --skill embedding-strategies -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/embedding-strategies, .gemini/skills/embedding-strategies, .github/skills/embedding-strategies and .opencode/skills/embedding-strategies in your project.

What does Embedding Strategies need to run?

SKILL.md names no scripts, command-line tools or credentials: Embedding Strategies is instructions for the agent only.

Does Embedding Strategies access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Embedding Strategies safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Embedding Strategies use?

Embedding Strategies is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Embedding Strategies use?

About 710 tokens (SKILL.md is roughly 2.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.2k tokens, read only when the agent opens those files.

What are the alternatives to Embedding Strategies?

Skills that share tags, products or a category with Embedding Strategies: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), RAG Architect (Jeffallan/claude-skills, 12k stars), Embeddings via 9Router (decolua/9router, 31k stars) and Qianwenai Wiki (chujianyun/skills, 742 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Embedding Strategies?

wshobson (a GitHub user) maintains it in wshobson/agents, which has 40,314 GitHub stars. The repository holds 142 skills in this directory. The repository was last updated on October 5, 2026.

Source: wshobson/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.