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 skills add ihatesea69/HieuNghi-AI-Skills --skill sentence-transformersgit clone --depth 1 https://github.com/ihatesea69/HieuNghi-AI-SkillsWrote 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/ihatesea69/hieunghi-ai-skills/sentence-transformers)<a href="https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/sentence-transformers"><img src="https://agentmods.dev/badge/skills/ihatesea69/hieunghi-ai-skills/sentence-transformers/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/ihatesea69/hieunghi-ai-skills/sentence-transformers"><img src="https://agentmods.dev/badge/skills/ihatesea69/hieunghi-ai-skills/sentence-transformers.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 | $0.00067 | $0.01574 |
| Opus 5 | $0.00034 | $0.00787 |
| Sonnet 5 | $0.00013 | $0.00315 |
| Haiku 4.5 | $0.00007 | $0.00157 |
Grade A, and why
sentence-transformers 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 8d 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
100% identical to sentence-transformers — 0 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sentence Transformers - State-of-the-Art Embeddings
Python framework for sentence and text embeddings using transformers.
When to use Sentence Transformers
Use when:
- Need high-quality embeddings for RAG
- Semantic similarity and search
- Text clustering and classification
- Multilingual embeddings (100+ languages)
- Running embeddings locally (no API)
- Cost-effective alternative to OpenAI embeddings
Metrics:
- 15,700+ GitHub stars
- 5000+ pre-trained models
- 100+ languages supported
- Based on PyTorch/Transformers
Use alternatives instead:
- OpenAI Embeddings: Need API-based, highest quality
- Instructor: Task-specific instructions
- Cohere Embed: Managed service
Quick start
Installation
pip install sentence-transformers
Basic usage
from sentence_transformers import SentenceTransformer
# Load model
model = SentenceTransformer('all-MiniLM-L6-v2')
# Generate embeddings
sentences = [
"This is an example sentence",
"Each sentence is converted to a vector"
]
embeddings = model.encode(sentences)
print(embeddings.shape) # (2, 384)
# Cosine similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
print(f"Similarity: {similarity.item():.4f}")
Popular models
General purpose
# Fast, good quality (384 dim)
model = SentenceTransformer('all-MiniLM-L6-v2')
# Better quality (768 dim)
model = SentenceTransformer('all-mpnet-base-v2')
# Best quality (1024 dim, slower)
model = SentenceTransformer('all-roberta-large-v1')
Multilingual
# 50+ languages
model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
# 100+ languages
model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
Domain-specific
# Legal domain
model = SentenceTransformer('nlpaueb/legal-bert-base-uncased')
# Scientific papers
model = SentenceTransformer('allenai/specter')
# Code
model = SentenceTransformer('microsoft/codebert-base')
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 256 lines · 67 tokens per session scan A fca19457be4e
sentence-transformers is a skill published in the GitHub repository ihatesea69/HieuNghi-AI-Skills (3 stars, last pushed 6mo ago), licensed MIT. It adds 67 tokens to every session and 1,574 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to sentence-transformers, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
sentence-transformers
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production…
sentence-transformers
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production…
sentence-transformers
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production…
sentence-transformers
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production…
sentence-transformers
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production…
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.