Borrowing it
Nothing to install: this file belongs to sagar-shirwalkar/servicenow-atlas. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sagar-shirwalkar/servicenow-atlas/main/.agents/skills/embedding-strategies/SKILL.mdgit clone --depth 1 https://github.com/sagar-shirwalkar/servicenow-atlasWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sagar-shirwalkar/servicenow-atlas/embedding-strategies)<a href="https://agentmods.dev/skills/sagar-shirwalkar/servicenow-atlas/embedding-strategies"><img src="https://agentmods.dev/badge/skills/sagar-shirwalkar/servicenow-atlas/embedding-strategies/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sagar-shirwalkar/servicenow-atlas/embedding-strategies"><img src="https://agentmods.dev/badge/skills/sagar-shirwalkar/servicenow-atlas/embedding-strategies.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.04310 |
| Opus 5.5 | $0.00015 | $0.01724 |
| Sonnet 5.5 | $0.00007 | $0.00862 |
| Haiku 4.5 | $0.00004 | $0.00431 |
Grade A, and why
embedding-strategies scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to embedding-strategies — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 601 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
Template 1: Voyage AI Embeddings (Recommended for Claude)
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")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 601 lines · 37 tokens per session scan A d949436dadf3
embedding-strategies is a skill published in the GitHub repository sagar-shirwalkar/servicenow-atlas (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 4,310 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to embedding-strategies, differing in 8 lines, and is treated as a copy.
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