Best for
- Choosing embedding models for RAG
- Optimizing chunking strategies
- Fine-tuning embeddings for domains
Source profileQuality 74/100
wshobson/agents/plugins/llm-application-dev/skills/embedding-strategies/SKILL.md
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.
- Source repository stars
- 38,313
- Declared platforms
- 0
- Static risk flags
- 1
- Last source update
- 2026-07-22
- Source checked
- 2026-07-28
Decision brief
What it does—and where it fits
Guide to selecting and optimizing embedding models for vector search applications.
Not for
- Tasks that require unconfirmed production actions or broad system permissions.
- Environments where the pinned source and install steps cannot be inspected.
Compatibility matrix
Platform support, with evidence labels
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
Inspect first. Install second.
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
Source-detected install commandSource
npx skills add https://github.com/wshobson/agents --skill "plugins/llm-application-dev/skills/embedding-strategies"Safe inspection promptEditorial
Inspect the Agent Skill "embedding-strategies" from https://github.com/wshobson/agents/blob/c4b82b0ad771190355eb8e204b1329732a18449a/plugins/llm-application-dev/skills/embedding-strategies/SKILL.md at commit c4b82b0ad771190355eb8e204b1329732a18449a. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
What the source asks the agent to do
- 01
When to Use This Skill
Choosing embedding models for RAG
Choosing embedding models for RAGOptimizing chunking strategiesFine-tuning embeddings for domains - 02
Core Concepts
Review the “Core Concepts” section in the pinned source before continuing.
Review and apply the “Core Concepts” source section. - 03
1. Embedding Model Comparison (2026)
Review the “1. Embedding Model Comparison (2026)” section in the pinned source before continuing.
Review and apply the “1. Embedding Model Comparison (2026)” source section. - 04
2. Embedding Pipeline
Review the “2. Embedding Pipeline” section in the pinned source before continuing.
Review and apply the “2. Embedding Pipeline” source section.
Permission review
Static risk signals and limitations
Reads files
The documentation asks the agent to read local files, directories, or repositories.
Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates.Evidence record
Why each signal appears
EvidenceSourceComputedTestedEditorial
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 74/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 38,313 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Provenance and original SKILL.md
- Repository
- wshobson/agents
- Skill path
- plugins/llm-application-dev/skills/embedding-strategies/SKILL.md
- Commit
- c4b82b0ad771190355eb8e204b1329732a18449a
- License
- MIT
- Collected
- 2026-07-28
- Default branch
- main
View the original 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 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