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 techysy/10router --skill 10router-embeddingsgit clone --depth 1 https://github.com/techysy/10routerWrote 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/techysy/10router/10router-embeddings)<a href="https://agentmods.dev/skills/techysy/10router/10router-embeddings"><img src="https://agentmods.dev/badge/skills/techysy/10router/10router-embeddings.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 5.1 | $0.00066 | $0.00719 |
| Opus 5 | $0.00033 | $0.00360 |
| Sonnet 5 | $0.00013 | $0.00144 |
| Haiku 4.5 | $0.00007 | $0.00072 |
Grade A, and why
10router-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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl $TENROUTER_URL/v1/models/embedding | jq '.data[].id' This is a copy
88% identical to 9router-embeddings — 22 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
10Router — Embeddings
Requires TENROUTER_URL (and TENROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/techysy/10router/main/skills/10router/SKILL.md for setup.
Discover
curl $TENROUTER_URL/v1/models/embedding | jq '.data[].id'
# Per-model dimensions
curl "$TENROUTER_URL/v1/models/info?id=openai/text-embedding-3-small"
Endpoint
POST $TENROUTER_URL/v1/embeddings
| Field | Required | Notes |
|---|---|---|
model |
yes | from /v1/models/embedding |
input |
yes | string OR array of strings |
encoding_format |
no | float (default) / base64 |
dimensions |
no | OpenAI v3 only |
Examples
curl -X POST $TENROUTER_URL/v1/embeddings \
-H "Authorization: Bearer $TENROUTER_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"openai/text-embedding-3-small","input":["hello","world"]}'
JS:
const r = await fetch(`${process.env.TENROUTER_URL}/v1/embeddings`, {
method: "POST",
headers: { "Authorization": `Bearer ${process.env.TENROUTER_KEY}`, "Content-Type": "application/json" },
body: JSON.stringify({ model: "gemini/text-embedding-004", input: "RAG chunk text" }),
});
const { data } = await r.json();
console.log(data[0].embedding.length); // dimension
Response shape
{ "object": "list", "model": "openai/text-embedding-3-small",
"data": [
{ "object": "embedding", "index": 0, "embedding": [0.0123, -0.045, ...] },
{ "object": "embedding", "index": 1, "embedding": [...] }
],
"usage": { "prompt_tokens": 5, "total_tokens": 5 } }
Provider quirks
| Provider | Notes |
|---|---|
openai, openrouter, mistral, voyage-ai, fireworks, together, nebius, github, nvidia, jina-ai |
Native OpenAI shape — dimensions works only on OpenAI v3 (text-embedding-3-*) |
gemini, google_ai_studio |
Server auto-converts to embedContent/batchEmbedContents — send OpenAI shape |
openai-compatible-*, custom-embedding-* |
Custom baseUrl from credentials |
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 · 70 lines · 66 tokens per session scan A 20bce8141c8d
10router-embeddings is a skill published in the GitHub repository techysy/10router (24 stars, last pushed today), licensed MIT. It adds 66 tokens to every session and 719 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 88% identical to 9router-embeddings, differing in 22 lines, and is treated as a copy.
Other skills, from other repositories
keirouter-embeddings
Generate vector embeddings via KeiRouter /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
keirouter-chat
Chat / code generation via KeiRouter using OpenAI /v1/chat/completions or Anthropic /v1/messages format with streaming + auto-fallback combos. Use when the user wants to ask an LLM, generate code, summarize text, or run prompts through KeiRouter.
keirouter
Entry point for KeiRouter — local/remote AI gateway with OpenAI-compatible REST for chat, image, TTS, embeddings, web search, web fetch. Use when the user mentions KeiRouter, KEIROUTERURL, or wants AI without writing provider boilerplate. This skill covers setup + indexes capability skills; fetch the relevant…
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…
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…