Generates standardized AGENTS.md files to help AI coding agents understand and work with your repository.
Works with
Provides a template-driven approach for creating agent-focused documentation that complements README.md with technical setup, workflow, and testing instructions
Covers essential sections including project overview, setup commands, development workflow, testing, code style, build/deployment, and PR guidelines
Supports monorepo structures with guidance for creating AGENTS.md files
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncreate-agentsmdExecute the skills CLI command in your project's root directory to begin installation:
Fetches create-agentsmd from github/awesome-copilot and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate create-agentsmd. Access via /create-agentsmd in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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You are a 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.
AGENTS.md is a Markdown file that serves as a "README for agents" - a dedicated, predictable place to provide context and instructions to help AI coding agents work on your project. It complements README.md by containing detailed technical context that coding agents need but might clutter a human-focused README.
AGENTS.md in the repository rootUse this as a starting template and customize based on the specific project:
# AGENTS.md
## Project Overview
[Brief description of the project, its purpose, and key technologies]
## Setup Commands
- Install dependencies: `[package manager] install`
- Start development server: `[command]`
- Build for production: `[command]`
## Development Workflow
- [Development server startup instructions]
- [Hot reload/watch mode information]
- [Environment variable setup]
## Testing Instructions
- Run all tests: `[command]`
- Run unit tests: `[command]`
- Run integration tests: `[command]`
- Test coverage: `[command]`
- [Specific testing patterns or requirements]
## Code Style
- [Language and framework conventions]
- [Linting rules and commands]
- [Formatting requirements]
- [File organization patterns]
## Build and Deployment
- [Build process details]
- [Output directories]
- [Environment-specific builds]
- [Deployment commands]
## Pull Request Guidelines
- Title format: [component] Brief description
- Required checks: `[lint command]`, `[test command]`
- [Review requirements]
## Additional Notes
- [Any project-specific context]
- [Common gotchas or troubleshooting tips]
- [Performance considerations]
Here's a real example from the agents.md website:
# Sample AGENTS.md file
## Dev environment tips
- Use `pnpm dlx turbo run where <project_name>` to jump to a package instead of scanning with `ls`.
- Run `pnpm install --filter <project_name>` to add the package to your workspace so Vite, ESLint, and TypeScript can see it.
- Use `pnpm create vite@latest <project_name> -- --template react-ts` to spin up a new React + Vite package with TypeScript checks ready.
- Check the name field inside each package's package.json to confirm the right name—skip the top-level one.
## Testing instructions
- Find the CI plan in the .github/workflows folder.
- Run `pnpm turbo run test --filter <project_name>` to run every check defined for that package.
- From the package root you can just call `pnpm test`. The commit should pass all tests before you merge.
- To focus on one step, add the Vitest pattern: `pnpm vitest run -t "<test name>"`.
- Fix any test or type errors until the whole suite is green.
- After moving files or changing imports, run `pnpm lint --filter <project_name>` to be sure ESLint and TypeScript rules still pass.
- Add or update tests for the code you change, even if nobody asked.
## PR instructions
- Title format: [<project_name>] <Title>
- Always run `pnpm lint` and `pnpm test` before committing.
Analyze the project structure to understand:
Identify key workflows by examining:
Create comprehensive sections covering:
Include specific, actionable commands that agents can execute directly
Test the instructions by ensuring all commands work as documented
Keep it focused on what agents need to know, not general project information
For large monorepos:
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.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
github/awesome-copilot
github/awesome-copilot
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
create-agentsmd reduced setup friction for our internal harness; good balance of opinion and flexibility.
create-agentsmd reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: create-agentsmd is the kind of skill you can hand to a new teammate without a long onboarding doc.
create-agentsmd is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend create-agentsmd for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
create-agentsmd has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: create-agentsmd is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added create-agentsmd from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for create-agentsmd matched our evaluation — installs cleanly and behaves as described in the markdown.
We added create-agentsmd from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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