Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/eddiebelaval/squire/embedding-generatornpx skills add eddiebelaval/squire --skill embedding-generatorgit clone --depth 1 https://github.com/eddiebelaval/squireWrote 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/eddiebelaval/squire/embedding-generator)<a href="https://agentmods.dev/skills/eddiebelaval/squire/embedding-generator"><img src="https://agentmods.dev/badge/skills/eddiebelaval/squire/embedding-generator.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.01448 |
| Opus 5 | $0.00009 | $0.00724 |
| Sonnet 5 | $0.00003 | $0.00290 |
| Haiku 4.5 | $0.00002 | $0.00145 |
Grade A, and why
Embedding Generator 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Embedding Generator
The Embedding Generator skill helps you create, manage, and utilize text embeddings for semantic search, similarity matching, clustering, and classification tasks. It guides you through selecting appropriate embedding models, preprocessing text for optimal vectorization, and storing/querying embeddings efficiently.
Text embeddings transform words, sentences, or documents into dense numerical vectors that capture semantic meaning. Similar concepts end up close together in vector space, enabling powerful AI applications like semantic search, recommendations, and content understanding.
This skill covers everything from choosing the right model (OpenAI, Cohere, sentence-transformers, etc.) to implementing production-ready embedding pipelines with proper batching, caching, and quality validation.
Core Workflows
Workflow 1: Generate Embeddings for Text Corpus
- Analyze the text corpus:
- Content type (documents, sentences, queries)
- Average length and variation
- Language(s) present
- Domain specificity
- Select embedding model:
- Consider dimensionality vs performance tradeoff
- Match model to content type
- Evaluate cost and latency constraints
- Preprocess text:
- Clean and normalize
- Chunk long documents appropriately
- Handle special characters and formatting
- Generate embeddings with batching
- Validate quality with spot checks
- Store in appropriate vector database
Workflow 2: Choose Embedding Model
- Gather requirements:
- Use case (search, clustering, classification)
- Latency requirements
- Cost constraints
- Accuracy needs
- Compare models:
Model Dims Speed Quality Cost OpenAI text-embedding-3-small 1536 Fast Good $$ OpenAI text-embedding-3-large 3072 Fast Best $$$ Cohere embed-english-v3 1024 Fast Great $$ sentence-transformers 384-768 Varies Good Free Voyage AI 1024 Fast Great $$ - Benchmark on representative samples
- Document decision rationale
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.
- yesterday First seen · 188 lines · 17 tokens per session scan A 7f29c17cb418
Embedding Generator is a skill published in the GitHub repository eddiebelaval/squire (21 stars, last pushed 20d ago), licensed MIT. It adds 17 tokens to every session and 1,448 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
Embedding Generator
Generate and manage text embeddings for semantic search, clustering, and similarity tasks.
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vector-database-management
Comprehensive guide for managing vector databases including Pinecone, Weaviate, and Chroma for semantic search, RAG systems, and similarity-based applications.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
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Guides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high cardinality results'.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.