9Router routes requests from coding tools such as Claude Code, Codex, Cursor, and Cline to AI models from many providers. Developers use it to reduce token usage, track subscription capacity, and switch automatically between subscription, cheap, and free models. The catalogue includes nine skills and one instruction for 9Router.
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 decolua/9router --skill 9router-chatgit clone --depth 1 https://github.com/decolua/9routerWrote 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/decolua/9router/9router-chat)<a href="https://agentmods.dev/skills/decolua/9router/9router-chat"><img src="https://agentmods.dev/badge/skills/decolua/9router/9router-chat.svg" alt="Measured on agentmods" height="20"></a>- Snyk pass
- 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 20 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 Data Exfiltration · line 50 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.
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 | $0.00806 |
| Opus 5 | $0.00032 | $0.00403 |
| Sonnet 5 | $0.00013 | $0.00161 |
| Haiku 4.5 | $0.00006 | $0.00081 |
Grade A, and why
9router-chat 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 $NINEROUTER_URL/v1/models | jq '.data[].id' Copies of this mod
2 near-identical copies found in the catalogue:
- extremerouter-chat — 88% identical, 8 lines differ
- zenrouter-chat — 86% identical, 26 lines differ
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
9Router — Chat
Requires NINEROUTER_URL (and NINEROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/decolua/9router/refs/heads/master/skills/9router/SKILL.md for setup.
Endpoints
POST $NINEROUTER_URL/v1/chat/completions— OpenAI formatPOST $NINEROUTER_URL/v1/messages— Anthropic format
Discover
curl $NINEROUTER_URL/v1/models | jq '.data[].id'
# Per-model metadata (contextWindow, params)
curl "$NINEROUTER_URL/v1/models/info?id=openai/gpt-4o"
Combos (e.g. vip, mycodex) auto-fallback through multiple providers.
OpenAI format
curl -X POST $NINEROUTER_URL/v1/chat/completions \
-H "Authorization: Bearer $NINEROUTER_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"openai/gpt-5","messages":[{"role":"user","content":"Hi"}],"stream":false}'
JS (OpenAI SDK):
import OpenAI from "openai";
const client = new OpenAI({ baseURL: `${process.env.NINEROUTER_URL}/v1`, apiKey: process.env.NINEROUTER_KEY });
const res = await client.chat.completions.create({
model: "openai/gpt-5",
messages: [{ role: "user", content: "Hi" }],
stream: true,
});
for await (const chunk of res) process.stdout.write(chunk.choices[0]?.delta?.content || "");
Anthropic format
curl -X POST $NINEROUTER_URL/v1/messages \
-H "Authorization: Bearer $NINEROUTER_KEY" \
-H "anthropic-version: 2023-06-01" \
-H "Content-Type: application/json" \
-d '{"model":"cc/claude-opus-4-7","max_tokens":1024,"messages":[{"role":"user","content":"Hi"}]}'
Response shape
OpenAI (/v1/chat/completions):
{ "id": "chatcmpl-...", "object": "chat.completion", "model": "openai/gpt-5",
"choices": [{ "index": 0, "message": { "role": "assistant", "content": "Hello!" }, "finish_reason": "stop" }],
"usage": { "prompt_tokens": 8, "completion_tokens": 2, "total_tokens": 10 } }
Streaming (stream:true) emits SSE: data: {choices:[{delta:{content:"..."}}]}\n\n ... data: [DONE]\n\n.
Anthropic (/v1/messages):
{ "id": "msg_...", "type": "message", "role": "assistant", "model": "cc/claude-opus-4-7",
"content": [{ "type": "text", "text": "Hello!" }],
"stop_reason": "end_turn", "usage": { "input_tokens": 8, "output_tokens": 2 } }
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 · 74 lines · 63 tokens per session scan A 1c9db8504ffd
9router-chat is a skill published in the GitHub repository decolua/9router (27,783 stars, last pushed 2d ago), licensed MIT. It adds 63 tokens to every session and 806 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.
Other skills, from other repositories
guidance
Constrain LLM output with grammars; guarantee valid JSON.
outlines
Outlines: structured JSON/regex/Pydantic LLM generation.
instructor
Structured LLM outputs validated with Pydantic.
obliteratus
OBLITERATUS: abliterate LLM refusals (diff-in-means).
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
saelens
Train sparse autoencoders to interpret model features.