embedding-strategies

GEO / AI search

wshobson/agentsskills.sh ↗

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9,965

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Since we started

+1.1%

40 readings, about 2 hours apart. Not a live curve.

Our category

GEO / AI search

ours

Last read

Sep 3, 2026

from the directory

Our brief

ours

This skill helps users select and optimize embedding models for semantic search and Retrieval-Augmented Generation (RAG) applications. It provides templates and guidance on choosing appropriate models, optimizing chunking strategies, or reducing embedding dimensions for specific domains like code, finance, or law.

What it produces
  • List of floating-point embeddings (vectors)
What it needs
  • Text list/string to embed
  • Model name (e.g., voyage-3-large)
  • Optional dimension integer
Paid services

optional

The evidence shows that while some templates require paid API keys, others support local deployment using Sentence Transformers.

Registration

not detected

The evidence does not contain information regarding required registration.

Limits and human review

The skill cannot decide which embedding model is best without knowing the specific use case; it also provides templates for embedding but does not perform chunking or preprocessing itself.

Evidencestatic findingsplugins/llm-application-dev/skills/embedding-strategies/references/details.md:1-91plugins/llm-application-dev/skills/embedding-strategies/SKILL.md:1-66
static findingsstatic_finding
[{"code":"env_var","match":"VOYAGE_API_KEY","path":"references/details.md"}]
plugins/llm-application-dev/skills/embedding-strategies/references/details.md1–91 · excerpt truncated
# embedding-strategies — templates and worked examples

## Templates

### Template 1: Voyage AI Embeddings (Recommended for Claude)

```python
from langchain_voyageai import VoyageAIEmbeddings
from typing import List
import os

# Initialize Voyage AI embeddings (recommended by Anthropic for Claude)
embeddings = VoyageAIEmbeddings(
    model="voyage-3-large",
    voyage_api_key=os.environ.get("VOYAGE_API_KEY")
)

def get_embeddings(texts: List[str]) -> List[List[float]]:
    """Get embeddings from Voyage AI."""
    return embeddings.embed_documents(texts)

def get_query_embedding(query: str) -> List[float]:
    """Get single query embedding."""
    return embeddings.embed_query(query)

# Specialized models for domains
code_embeddings = VoyageAIEmbeddings(model="voyage-code-3")
finance_embeddings = VoyageAIEmbeddings(model="voyage-finance-2")
legal_embeddings = VoyageAIEmbeddings(model="voyage-law-2")
```

### Template 2: OpenAI Embeddings

```python
from openai import OpenAI
from typing import List
import numpy as np

client = OpenAI()

def get_embeddings(
    texts: List[str],
    model: str = "text-embedding-3-small",
    dimensions: int = None
) -> List[List[float]]:
    """Get e
plugins/llm-application-dev/skills/embedding-strategies/SKILL.md1–66
---
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 

Read 3 of 3 text files in the skill.

Checked September 2026 · GemmaWritten from the skill's published files. Check the source before relying on access or cost details.

Installfrom the directory

npx skills add https://github.com/wshobson/agents

Installing happens there, not here. We are an index with an opinion, not a mirror.

What is inside itfrom the directory

references/details.md
SKILL.md

2 files — names only. The directory does not report sizes.

What the auditors foundfrom the directory

A skill is instructions your agent will follow and scripts it may run, so who checked it matters as much as how many people installed it.

Gen Agent Trust HubThe skill provides comprehensive guides and code templates for selecting and optimizing embedding models and chunking strategies. It uses standard, well-known AI libraries and follows best practices for handling API keys through environment variables.Mar 7, 2026 · SAFEpass
SocketNo alertsMar 18, 2026pass
SnykRisk: LOW · No issuesMar 7, 2026 · LOWpass
Runlayer1 file scanned · No issuesMar 7, 2026 · NONEpass
ZeroLeaksScore: 93/100 · 2 sections analyzedApr 16, 2026 · NONEpass

Installs, reading by readingours

9.9k
10k
Aug 30, 202640 readings over 5 days, drawn as the last reading of each day.Sep 3, 2026

Axis starts at 9.9k, not zero — the range is 9.9k to 10k.

Asked out loudspoken, not typed

The same skill in the words people use speaking to an assistant rather than typing into a box. Each one carries the situation it came from, and each answer says only what the skill's own files support.

deciding which skill to useDo I need to pay for this?

It depends on the embedding model you choose; some templates use paid APIs that require an API key, while others support local models using Sentence Transformers.

setting up the environmentDo I need to register anything?

The evidence does not specify any registration requirements for this skill.

Filed alongside itours