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.
git clone --depth 1 https://github.com/Azure-Samples/mcp-agent-langchainjsWrote 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/mcp-agent-langchainjs/create-agents.md)<a href="https://agentmods.dev/commands/azure-samples/mcp-agent-langchainjs/create-agents.md"><img src="https://agentmods.dev/badge/commands/azure-samples/mcp-agent-langchainjs/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/mcp-agent-langchainjs/create-agents.md"><img src="https://agentmods.dev/badge/commands/azure-samples/mcp-agent-langchainjs/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 | $0.01028 |
| Opus 5.5 | $0.00004 | $0.00411 |
| Sonnet 5.5 | $0.00002 | $0.00206 |
| 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 today.
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 — 103 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.
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.
- today First seen · 103 lines · 11 tokens per session scan A ad9892087ecf
create-agents.md is a command published in the GitHub repository Azure-Samples/mcp-agent-langchainjs (204 stars, last pushed today), licensed MIT. It adds 11 tokens to every session and 1,028 once invoked, about $0.0000 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 2026-10-29.
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clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
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