agents-md-generator
Generate comprehensive agents.md files for Builder.io Fusion projects. Creates project-specific AI instruction files that establish conventions, build commands, testing procedures, design system rules, and coding standards. Use when setting up a new project, onboarding a repository to AI-assisted development, or improving AI code generation quality.
Best use case
agents-md-generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate comprehensive agents.md files for Builder.io Fusion projects. Creates project-specific AI instruction files that establish conventions, build commands, testing procedures, design system rules, and coding standards. Use when setting up a new project, onboarding a repository to AI-assisted development, or improving AI code generation quality.
Teams using agents-md-generator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agents-md-generator-jthoms1/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agents-md-generator Compares
| Feature / Agent | agents-md-generator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate comprehensive agents.md files for Builder.io Fusion projects. Creates project-specific AI instruction files that establish conventions, build commands, testing procedures, design system rules, and coding standards. Use when setting up a new project, onboarding a repository to AI-assisted development, or improving AI code generation quality.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# agents.md Generator
You are a specialist in creating `agents.md` files—the configuration files that Builder.io Fusion uses to understand project conventions. A well-crafted agents.md dramatically improves code generation quality by teaching the AI your team's patterns, preferences, and requirements.
## Determine the Workflow
Use AskUserQuestion to clarify which workflow the user needs:
1. **Generate New** - Create agents.md for a project that doesn't have one
2. **Update Existing** - Improve or expand an existing agents.md
3. **Analyze Only** - Review the project and provide recommendations without generating
If the user's intent is clear from their message, proceed directly.
## Quick Start
1. **Check for existing file**: Look for `agents.md` at the repository root
2. **Analyze the repository**: Examine existing patterns, dependencies, and configuration
3. **Identify the project type**: Framework, language, styling approach, testing setup
4. **Read specialized template**: Use the appropriate template for the project type
5. **Generate the agents.md**: Create a comprehensive file at repository root
6. **Validate**: Verify all commands work and paths are correct
## Why agents.md Matters
Without clear instructions, AI assistants guess at conventions. With a good agents.md, generated code looks like your team wrote it. The file should:
- Establish coding standards and naming conventions
- Document build, test, and dev commands
- Specify design system components and usage rules
- Define approved/forbidden dependencies
- List common pitfalls to avoid
## Section Priority Guide
| Section | Purpose | Priority |
|---------|---------|----------|
| Project Overview | Context about the app/repo | Required |
| Dev Environment | Setup, install, run commands | Required |
| Code Style | Formatting, naming, patterns | Required |
| Design System | Components, tokens, usage rules | High |
| Testing | Test commands, coverage requirements | High |
| File Structure | Where things go | Medium |
| Dependencies | What to use, what to avoid | Medium |
| Common Pitfalls | Mistakes to avoid | Medium |
| Git Workflow | Branching, commits, PRs | Optional |
## Specialized Resources
Read the appropriate template based on project type:
| Project Type | Resource | When to Use |
|-------------|----------|-------------|
| Monorepo | `monorepo-template.md` | Turborepo, Nx, pnpm workspaces |
| Next.js App Router | `nextjs-app-router-template.md` | Next.js 13+ with app directory |
| Standard project | `assets/complete-example.md` | General reference for any project |
## Repository Analysis Workflow
Before generating an agents.md, analyze the codebase systematically:
### Step 1: Package Manager & Scripts
Examine `package.json` for:
- Package manager (npm, pnpm, yarn, bun)
- Scripts: dev, build, test, lint commands
- Key dependencies (framework, styling, testing)
### Step 2: Framework & Structure
Identify by checking for these directories and files:
- `src/`, `app/`, `pages/`, `components/` directories
- Config files: `.eslintrc*`, `.prettierrc*`, `tsconfig.json`, `tailwind.config.*`, `biome.json`
- Framework indicators: `next.config.*`, `vite.config.*`, `nuxt.config.*`
Look for:
- React, Vue, Svelte, or other framework
- App Router vs Pages Router (Next.js)
- TypeScript configuration
- Styling approach (Tailwind, CSS Modules, etc.)
### Step 3: Design System
Search for:
- Design system imports (patterns like `from '@company/ui'`)
- Component library references in package.json (shadcn, radix, mui, chakra, mantine)
- Design token files or CSS variables
### Step 4: Testing Setup
Identify by looking for:
- Test files: `*.test.*`, `*.spec.*`
- Test config: `jest.config.*`, `vitest.config.*`, `playwright.config.*`
- Test libraries in package.json (testing-library, jest, vitest, playwright)
### Step 5: Monorepo Detection
Check for monorepo indicators:
- `turbo.json`, `nx.json`, `pnpm-workspace.yaml`, `lerna.json`
- `packages/`, `apps/`, `libs/` directories
- Workspaces configuration in package.json
If monorepo detected, read `monorepo-template.md` for additional sections.
## agents.md Template Structure
Generate the file at the repository root as `agents.md` with these sections:
```markdown
# agents.md
## Project Overview
[Brief description of what this application does]
**Tech Stack:**
- Framework: [Next.js 14 / React 18 / Vue 3 / etc.]
- Language: [TypeScript / JavaScript]
- Styling: [Tailwind CSS / CSS Modules / etc.]
- Testing: [Jest / Vitest / Playwright / etc.]
---
## Dev Environment
### Setup
[Package manager] install
cp .env.example .env.local
### Common Commands
| Command | Purpose |
|---------|---------|
| `[pm] dev` | Start development server |
| `[pm] build` | Production build |
| `[pm] test` | Run test suite |
| `[pm] lint` | Run linter |
---
## Code Style
### Naming Conventions
| Type | Convention | Example |
|------|------------|---------|
| Components | PascalCase | `UserProfile.tsx` |
| Hooks | camelCase with use prefix | `useAuth.ts` |
| Utilities | camelCase | `formatDate.ts` |
### File Organization
[Directory structure]
---
## Design System
[If applicable - component library, usage rules, tokens]
---
## Testing
[Test patterns, requirements, file locations]
---
## Common Pitfalls
[Project-specific mistakes to avoid]
```
See `assets/complete-example.md` for a fully-fleshed example.
## Handling Existing agents.md
If the project already has an `agents.md`:
1. **Read and analyze** the existing file
2. **Identify gaps** - missing sections, outdated commands, vague rules
3. **Propose updates** - show what would be added or changed
4. **Ask before replacing** - confirm with user before overwriting
## Validation Checklist
Before finalizing an agents.md, verify:
- [ ] File is named `agents.md` (lowercase) at repository root
- [ ] Package manager commands match actual scripts in package.json
- [ ] Build and dev commands actually exist
- [ ] Design system package name is accurate (if referenced)
- [ ] File structure matches actual repository
- [ ] No references to non-existent packages or files
- [ ] Under 500 lines total
## Best Practices
### Do:
- Start simple, add detail based on actual AI behavior issues
- Use specific file paths and real examples from the codebase
- Include actual component names from the design system
- Reference real configuration files (tsconfig paths, etc.)
- Update when conventions change
### Don't:
- Write vague guidance ("write clean code")
- Create rules that conflict with each other
- Exceed 500 lines—keep it focused
- Include sensitive information (API keys, internal URLs)
- Duplicate information that's in other config files
## Iteration Pattern
After creating the initial agents.md:
1. Generate code using the AI
2. Note where AI deviates from conventions
3. Add specific rules to address deviations
4. Repeat until AI output matches expectations
## Resources
| Resource | When to Use |
|----------|-------------|
| `assets/complete-example.md` | Full reference example |
| `monorepo-template.md` | Turborepo/Nx/pnpm workspaces |
| `nextjs-app-router-template.md` | Next.js 13+ App Router |
## Output Format
When generating an agents.md, provide:
1. **Analysis Summary**: Key findings from repository analysis
2. **Generated agents.md**: The complete file content
3. **Validation Notes**: Any commands to verify or potential issues foundRelated Skills
declarative-agents-microsoft365
Comprehensive development guidelines for Microsoft 365 Copilot declarative agents with schema v1.5, TypeSpec integration, and Microsoft 365 Agents Toolkit workflows Triggers on: **.json, **.ts, **.tsp, **manifest.json, **agent.json, **declarative-agent.json
debate-persona-generator
Generates three distinct expert challenger personas for multi-perspective debate. Each persona critiques from a different angle.
copilot-instructions-generator
Generate and maintain high-quality GitHub Copilot instruction files (.github/copilot-instructions.md). Use this skill when asked to create copilot instructions, generate copilot-instructions.md, set up copilot config, or update copilot instructions for any project or tech stack.
copilot-instructions-blueprint-generator
Technology-agnostic blueprint generator for creating comprehensive copilot-instructions.md files that guide GitHub Copilot to produce code consistent with project standards, architecture patterns, and exact technology versions by analyzing existing codebase patterns and avoiding assumptions.
Conventional Commit Generator
This skill should be used when the user asks to "create a conventional commit", "generate conventional commits", "commit with conventional format", "group my changes for commits", "make a conventional commit message", or mentions "semantic commits", "commitizen", "commit conventions". Analyzes staged and unstaged changes, groups related modifications, and generates properly formatted conventional commit messages with interactive commit grouping options.
consensus-persona-generator
Generate and persist reusable persona panels (persona_set artifacts) for consensus decision workflows. This skill initializes lightweight multi-agent disagreement with weighted reputations so downstream guards can make auditable, policy-governed decisions.
chapter-outline-generator
Generate structured chapter outlines for books with plot points, character arcs, word counts, and pacing notes. Use when planning book chapters, structuring narratives, or organizing story flow.
bash-script-generator
Comprehensive toolkit for generating best practice bash scripts following current standards and conventions. Use this skill when creating new bash scripts, implementing shell automation, text processing workflows, or building production-ready command-line tools.
baml-generator
Automatically regenerate BAML client code when .baml files are modified. Use after any changes to BAML definitions to keep generated code in sync.
awesome-copilot-root-excalidraw-diagram-generator
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw. Use when: the task directly matches excalidraw diagram generator responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
awesome-copilot-root-architecture-blueprint-generator
Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development. Use when: the task directly matches architecture blueprint generator responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
astro-page-generator
Generate Astro page components with islands architecture. Triggers on "create astro page", "generate astro component", "astro file", ".astro page".