open-responses-agent-skill: Instructions file for Codex

AGENTS.md

open-responses-agent-skill AGENTS.md is an instructions file for Codex, OpenCode from OthmanAdi/open-responses-agent-skill. It costs 964 tokens per session, scanned A, original, MIT.

Development instructions for building autonomous agents with the Open Responses API through Hugging Face's model-provider router. The router is one web endpoint that lets a request select a provider by adding a suffix to the model name.

In plain words
What is it for?
Use it when implementing an agent that sends instructions, user input, tools, and reasoning settings to the Open Responses endpoint and handles its returned messages or function calls.
Why use it?
It explains the request and response formats and how to choose among hosted model providers. This gives developers a consistent way to connect an agent to different models.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md.

This is OthmanAdi/open-responses-agent-skill's own configuration. It tells Codex and OpenCode how to work on open-responses-agent-skill itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything open-responses-agent-skill configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/OthmanAdi/open-responses-agent-skill/master/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/OthmanAdi/open-responses-agent-skill

Made for: Codex, OpenCode.

Wrote 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.

agentmods badge for open-responses-agent-skill AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/othmanadi/open-responses-agent-skill/agents-md/github.svg)](https://agentmods.dev/instructions/othmanadi/open-responses-agent-skill/agents-md)
Your own site
<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.

agentmods 80×15 button for open-responses-agent-skill AGENTS.md

Your own site · 80×15
<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>
Per session 964 This file is loaded in full into every session.
When invoked 964 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2mo ago against content hash a3648b89fb57, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

AGENTS.md · 134 lines

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" }

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);
}

Read the full file on GitHub · 134 lines

Changes

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.

  1. 2mo ago First seen · 134 lines · 964 tokens per session scan A a3648b89fb57

Subscribe to this mod's changes

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.

Related

Other instructions, from other repositories

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 9,715 tokens

next.js AGENTS.md

AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,461 tokens

vscode oss-third-party-notices.instructions.md

Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).

microsoft/vscode · 5,262 tokens

langchain AGENTS.md

AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.

langchain-ai/langchain · 4,680 tokens

vscode copilot-instructions.md

Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).

microsoft/vscode · 2,750 tokens

deno copilot-instructions.md

Copilot instructions for denoland/deno, covering deno development guide for github copilot, network access, high level overview, key directories and key files to understand first.

denoland/deno · 3,077 tokens