Borrowing it
Nothing to install: this file belongs to Azure-Samples/azure-openai-rag-workshop. 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/Azure-Samples/azure-openai-rag-workshop/main/.github/prompts/create-agents.md.prompt.mdgit clone --depth 1 https://github.com/Azure-Samples/azure-openai-rag-workshopWrote 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/commands/azure-samples/azure-openai-rag-workshop/create-agents.md)<a href="https://agentmods.dev/commands/azure-samples/azure-openai-rag-workshop/create-agents.md"><img src="https://agentmods.dev/badge/commands/azure-samples/azure-openai-rag-workshop/create-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/commands/azure-samples/azure-openai-rag-workshop/create-agents.md"><img src="https://agentmods.dev/badge/commands/azure-samples/azure-openai-rag-workshop/create-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 | $0.00011 | $0.01027 |
| Opus 5.5 | $0.00004 | $0.00411 |
| Sonnet 5.5 | $0.00002 | $0.00205 |
| Haiku 4.5 | $0.00001 | $0.00103 |
Grade A, and why
create-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.
This is a copy
100% identical to create-agents.md — 33 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create high‑quality AGENTS.md file
- Input: Any files that may provide context about the project, including but not limited to README files, documentation, configuration files (e.g., package.json, pyproject.toml, etc.), CI/CD workflows and any other relevant files.
Role
You are an expert software architect and code agent. Your task is to create a complete, accurate AGENTS.md at the root of this repository that follows the public guidance at https://agents.md/.
AGENTS.md is an open format designed to provide coding agents with the context and instructions they need to work effectively on a project.
Goal
Based on your analysis of the project, your goal is to generate these:
- Create or update
AGENTS.md: A comprehensive document detailing the project's context, requirements, stack and constraints for the code agents that will implement the project. - There must at least one
AGENTS.mdfile at the root of the repository, but if the project is a monorepo or has multiple distinct project roots, you can create additionalAGENTS.mdfiles in each relevant subdirectories.
When creating the AGENTS.md file, prioritize clarity, completeness, and actionability. The goal is to give any coding agent enough context to effectively contribute to the project without requiring additional human guidance.
Instructions
- Examine the current project to understand its context, requirements, constraints, architecture, tech stack and specificities, and any existing files that may provide insights.
- For example, look for files that may contain the project name, idea, vision, requirements, technology stack and constraints. This may include README files, project documentation, configuration files (e.g., package.json, pyproject.toml, etc.), CI/CD workflows and any other relevant files.
- If the project is a monorepo or has multiple distinct project roots, identify the relevant subdirectories that may require their own
AGENTS.mdfiles.
- Once you have all the necessary information, create or update the
AGENTS.mdfile with all relevant project context, requirements, stack and constraints for the code agents that will implement the project.
- When doing so, use this the template for structuring the document:
# [project_name] [Project summary] ## Overview - [Brief description of what the project does, its purpose and audience] - [Architecture overview if complex] - [Project structure if relevant] ## Key Technologies and Frameworks - [List of main technologies, frameworks, and libraries used in the project] ## Constraints and Requirements [if any] - [Any specific constraints, requirements, or considerations for the project] ## Challenges and Mitigation Strategies [if any] - [Potential challenges and how they will be addressed] ## Development Workflow [if applicable] - [Most important scripts, commands, and tools for development, testing, and deployment. How to start dev server, run tests, build for production, etc.] ## Coding Guidelines [if any] - [Any specific coding standards, style guides, or best practices to follow] ## Security Considerations [if any] - [Any security practices or considerations relevant to the project] ## Pull Request Guidelines [if any] - [Any specific guidelines for creating pull requests, such as, title format, required checks, review process, commit message conventions, etc.] ## Debugging and Troubleshooting [if applicable] - [Common issues and solutions, logging patterns, debug configuration, performance considerations] - If a section is not relevant, you can omit it.
- Be specific and concise: include exact commands, and information from the provided context, do not make any assumptions or add unnecessary details.
- Only use information you found to fill the sections.
- Use standard Markdown formatting.
- If needed, you can add specific sections relevant to the project that are not covered by the template if they provide important context for the code agents.
- If the file already contains enough relevant information, you can skip this step.
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 · 88 lines · 11 tokens per session scan A aa6c2388fd74
create-agents.md is a command published in the GitHub repository Azure-Samples/azure-openai-rag-workshop (154 stars, last pushed 15d ago), licensed MIT. It adds 11 tokens to every session and 1,027 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to create-agents.md, differing in 33 lines, and is treated as a copy.
create-agents.md compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| create-agents.mdAzure-Samples/azure-openai-rag-workshop | Command | 154 | 15d ago | MIT | A | 11 | GitHub Copilot |
| awesome-chatgpttaishi-i/awesome-ChatGPT-repositories | Command | 3.4k | yesterday | CC0-1.0 | A | 0 | Claude Code |
| git-commitChatLunaLab/chatluna | Command | 446 | 9d ago | AGPL-3.0 | A | 4 | Claude Code |
| github-prChatLunaLab/chatluna | Command | 446 | 9d ago | AGPL-3.0 | A | 5 | Claude Code |
| checklistgithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |