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 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/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/mydisha/keirouter/keirouter-embeddings"><img src="https://agentmods.dev/badge/skills/mydisha/keirouter/keirouter-embeddings.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 32 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Agent Snooping · line 8 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00070 | $0.00725 |
| Opus 5.5 | $0.00028 | $0.00290 |
| Sonnet 5.5 | $0.00014 | $0.00145 |
| Haiku 4.5 | $0.00007 | $0.00072 |
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 10d 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 — 73 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 |
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, Cohere, Voyage, Jina, Fireworks, Together, Nebius, NVIDIA, OpenRouter, Venice, Cloudflare, vLLM | OpenAI shape — dimensions works only on models that support it (e.g. OpenAI text-embedding-3-*) |
| AWS Bedrock | Titan and Cohere Embed via InvokeModel; send OpenAI shape |
| Azure AI Foundry, Databricks, GitHub Models | OpenAI shape on the provider's own endpoint |
| GigaChat | Native connector; send OpenAI shape |
| Custom OpenAI | Any OpenAI-compatible /embeddings endpoint |
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.
- 10d ago Changed · +3 lines · +7 tokens per session 5e1ba62d9559
- 1mo ago First seen · 70 lines · 63 tokens per session scan A 817d6488c917
keirouter-embeddings is a skill published in the GitHub repository mydisha/keirouter (153 stars, last pushed 3d ago), licensed MIT. It adds 70 tokens to every session and 725 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
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.
10router-embeddings
Generate vector embeddings via 10Router /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.
qdrant
Vector search engine for production RAG systems.
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
chroma
Embedding database for RAG and semantic search.
pinecone
Managed vector DB for production RAG and search.