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/mydisha/keirouter/keirouter-embeddingsnpx skills add mydisha/keirouter --skill keirouter-embeddingsgit clone --depth 1 https://github.com/mydisha/keirouterWrote 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/mydisha/keirouter/keirouter-embeddings)<a href="https://agentmods.dev/skills/mydisha/keirouter/keirouter-embeddings"><img src="https://agentmods.dev/badge/skills/mydisha/keirouter/keirouter-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 | $0.00063 | $0.00676 |
| Opus 5 | $0.00032 | $0.00338 |
| Sonnet 5 | $0.00013 | $0.00135 |
| Haiku 4.5 | $0.00006 | $0.00068 |
Grade A, and why
keirouter-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 6d 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 $KEIROUTER_URL/v1/models/embedding | jq '.data[].id' 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.
KeiRouter — Embeddings
Requires KEIROUTER_URL (and KEIROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/mydisha/keirouter/main/skills/keirouter/SKILL.md for setup.
Discover
curl $KEIROUTER_URL/v1/models/embedding | jq '.data[].id'
# Per-model dimensions
curl "$KEIROUTER_URL/v1/models/info?id=openai/text-embedding-3-small"
Endpoint
POST $KEIROUTER_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 $KEIROUTER_URL/v1/embeddings \
-H "Authorization: Bearer $KEIROUTER_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"openai/text-embedding-3-small","input":["hello","world"]}'
JS:
const r = await fetch(`${process.env.KEIROUTER_URL}/v1/embeddings`, {
method: "POST",
headers: { "Authorization": `Bearer ${process.env.KEIROUTER_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 quick reference
| Provider | Notes |
|---|---|
| OpenAI, Mistral, Voyage, Fireworks, Together, Nebius, NVIDIA, Jina | Native OpenAI shape — dimensions works only on OpenAI v3 (text-embedding-3-*) |
| Gemini | Server auto-converts to embedContent/batchEmbedContents — send OpenAI shape |
| Custom OpenAI | Custom baseUrl from credentials |
Batch (input as array) is faster; some providers cap batch size.
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.
- 6d ago First seen · 70 lines · 63 tokens per session scan A 817d6488c917
keirouter-embeddings is a skill published in the GitHub repository mydisha/keirouter (129 stars, last pushed 8d ago), licensed MIT. It adds 63 tokens to every session and 676 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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