Borrowing it
Nothing to install: this file belongs to OthmanAdi/open-responses-agent-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/OthmanAdi/open-responses-agent-skill/master/AGENTS.mdgit clone --depth 1 https://github.com/OthmanAdi/open-responses-agent-skillWrote 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/instructions/othmanadi/open-responses-agent-skill/agents-md)<a href="https://agentmods.dev/instructions/othmanadi/open-responses-agent-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/othmanadi/open-responses-agent-skill/agents-md/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/instructions/othmanadi/open-responses-agent-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/othmanadi/open-responses-agent-skill/agents-md.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 | $0.00964 | $0.00964 |
| Opus 5.5 | $0.00386 | $0.00386 |
| Sonnet 5.5 | $0.00193 | $0.00193 |
| Haiku 4.5 | $0.00096 | $0.00096 |
Grade A, and why
open-responses-agent-skill AGENTS.md scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open Responses Agent Development
About This Skill
Build autonomous agents using the Open Responses API via the HuggingFace Inference Providers router.
Core Knowledge
Single Unified Endpoint
All requests go to ONE endpoint:
https://router.huggingface.co/v1/responses
Provider selection is done via MODEL SUFFIX (not separate URLs).
Provider Selection via Model Suffix
moonshotai/Kimi-K2-Instruct-0905:groq # Groq (fast inference)
meta-llama/Llama-3.1-70B-Instruct:together # Together AI
meta-llama/Llama-3.1-70B-Instruct:nebius # Nebius (EU infrastructure)
meta-llama/Llama-3.1-70B-Instruct:auto # Auto selection
Request Structure
{
"model": "moonshotai/Kimi-K2-Instruct-0905:groq",
"instructions": "You are a helpful assistant.",
"input": "User's task",
"tools": [...],
"tool_choice": "auto",
"reasoning": { "effort": "medium" }
}
Response Structure
{
"id": "resp_abc123",
"model": "moonshotai/Kimi-K2-Instruct-0905",
"output": [
{ "type": "reasoning", "content": "..." },
{ "type": "function_call", "name": "...", "arguments": {...} },
{ "type": "function_call_output", "output": "..." },
{ "type": "message", "content": "..." }
],
"output_text": "convenience helper",
"usage": { "input_tokens": 100, "output_tokens": 200 }
}
Tool Definition Format
Tools are defined at TOP LEVEL - NOT nested in function:
{
"type": "function",
"name": "search",
"description": "Search for information",
"parameters": { ... }
}
Reasoning Visibility
| Level | Field | Providers |
|---|---|---|
| RAW | content |
Groq, Together, Nebius (open weight) |
| SUMMARY | summary |
Some proprietary models |
| ENCRYPTED | encrypted_content |
Most proprietary models |
Control with: "reasoning": { "effort": "low" | "medium" | "high" }
SDK Usage (Recommended)
TypeScript
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://router.huggingface.co/v1",
apiKey: process.env.HF_TOKEN,
});
const response = await client.responses.create({
model: "moonshotai/Kimi-K2-Instruct-0905:groq",
instructions: "You are a helpful assistant.",
input: "Your task here",
});
console.log(response.output_text);
for (const item of response.output) {
console.log(item.type, item);
}
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 · 134 lines · 964 tokens per session scan A a3648b89fb57
open-responses-agent-skill AGENTS.md is an instructions file published in the GitHub repository OthmanAdi/open-responses-agent-skill (3 stars, last pushed 9mo ago), licensed MIT. It adds 964 tokens to every session, about $0.0039 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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