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 0xatd/cheaptokens-skills --skill venice-embeddingsgit clone --depth 1 https://github.com/0xatd/cheaptokens-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/0xatd/cheaptokens-skills/venice-embeddings)<a href="https://agentmods.dev/skills/0xatd/cheaptokens-skills/venice-embeddings"><img src="https://agentmods.dev/badge/skills/0xatd/cheaptokens-skills/venice-embeddings/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/0xatd/cheaptokens-skills/venice-embeddings"><img src="https://agentmods.dev/badge/skills/0xatd/cheaptokens-skills/venice-embeddings.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 | $0.00051 | $0.01488 |
| Opus 5.5 | $0.00020 | $0.00595 |
| Sonnet 5.5 | $0.00010 | $0.00298 |
| Haiku 4.5 | $0.00005 | $0.00149 |
Grade A, and why
venice-embeddings scanned grade A with 1 finding 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl https://api.venice.ai/api/v1/embeddings \ This is a copy
100% identical to venice-embeddings — 96 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Venice Embeddings
POST /api/v1/embeddings returns vector embeddings for strings. It's OpenAI-compatible: the request and response match https://api.openai.com/v1/embeddings closely enough that the OpenAI SDK works out of the box with baseURL: "https://api.venice.ai/api/v1".
Use when
- You're building retrieval / RAG / similarity search.
- You need text clustering, classification, deduplication, or reranking.
- You want Venice's "no-training, no-retention" stance on inference inputs — embeddings are generated and returned; the API does not publish E2EE semantics on
/embeddingsthe way it does on selected chat models.
Text-only. For image/multimodal signals, either run images through a vision chat model and embed the description, or pick a multimodal-capable embedding model from GET /models?type=embedding (the catalog changes; inspect model_spec on each row).
Minimal request
curl https://api.venice.ai/api/v1/embeddings \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-H "Accept-Encoding: gzip, br" \
-d '{
"model": "text-embedding-bge-m3",
"input": "Why is the sky blue?"
}'
{
"object": "list",
"model": "text-embedding-bge-m3",
"data": [
{ "object": "embedding", "index": 0, "embedding": [0.0023, -0.0093, 0.0158, ...] }
],
"usage": { "prompt_tokens": 8, "total_tokens": 8 }
}
Request schema
| Field | Type | Notes |
|---|---|---|
model |
string | Required. Model ID from GET /models?type=embedding. |
input |
string | string[] | number[] | number[][] | Required. Single string, array of strings (≤ 2048 entries), or pre-tokenized arrays. |
encoding_format |
"float" | "base64" |
Default "float". Use "base64" for ~4× payload shrinkage; decode client-side. |
dimensions |
integer | Optional. Truncate output dimensions. Only meaningful when the model's model_spec.supportsCustomDimensions === true — behavior on non-supporting models is model-dependent; test a small call before relying on it. |
user |
string | Accepted for OpenAI compat. Discarded by Venice. |
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.
- 2mo ago First seen · 130 lines · 51 tokens per session scan A af5cb8f4a932
venice-embeddings is a skill published in the GitHub repository 0xatd/cheaptokens-skills (2 stars, last pushed 13d ago), licensed MIT. It adds 51 tokens to every session and 1,488 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to venice-embeddings, differing in 96 lines, and is treated as a copy.
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