write-claude-md
About
This skill generates a CLAUDE.md file with project-specific instructions for AI coding assistants. It helps standardize AI behavior by documenting conventions, constraints, and integration patterns like MCP servers. Use it when starting new projects or improving AI assistance on existing codebases.
Quick Install
Claude Code
Recommendednpx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/write-claude-mdCopy and paste this command in Claude Code to install this skill
Documentation
Write CLAUDE.md
Create a CLAUDE.md file that gives AI assistants effective project-specific context.
When to Use
- Starting a new project where AI assistants will be used
- Improving AI assistant behavior on an existing project
- Documenting project conventions, workflows, and constraints
- Integrating MCP servers or agent definitions into a project
Inputs
- Required: Project type and technology stack
- Required: Key conventions and constraints
- Optional: MCP server configurations
- Optional: Author and contributor information
- Optional: Security and confidentiality requirements
Procedure
Step 1: Create Basic CLAUDE.md
Place CLAUDE.md in the project root:
# Project Name
Brief description of what this project is and its purpose.
## Quick Start
Essential commands for working on this project:
```bash
# Install dependencies
npm install # or renv::restore() for R
# Run tests
npm test # or devtools::test() for R
# Build
npm run build # or devtools::check() for R
Architecture
Key architectural decisions and patterns used in this project.
Conventions
- Always use descriptive variable names
- Follow [language-specific style guide]
- Write tests for all new functionality
**Got:** A `CLAUDE.md` file exists in the project root with at minimum a project description, quick start commands, architecture overview, and conventions section.
**If fail:** If unsure what to include, start with the Quick Start section containing the three most important commands (install, test, build). The file can be expanded incrementally as the project evolves.
### Step 2: Add Technology-Specific Sections
**For R packages**:
```markdown
## Development Workflow
```r
devtools::load_all() # Load for development
devtools::document() # Regenerate docs
devtools::test() # Run tests
devtools::check() # Full package check
Package Structure
R/- Source code (one function per file)tests/testthat/- Tests mirror R/ structurevignettes/- Long-form documentationman/- Generated by roxygen2 (do not edit manually)
Critical Files (Do Not Delete)
.Rprofile- Session configuration.Renviron- Environment variables (git-ignored)renv.lock- Locked dependencies
**For Node.js/TypeScript**:
```markdown
## Stack
- Next.js 15 with App Router
- TypeScript strict mode
- Tailwind CSS for styling
- Vercel for deployment
## Conventions
- Use `@/` import alias for src/ directory
- Server Components by default, `"use client"` only when needed
- API routes in `src/app/api/`
Got: Technology-specific sections are added that match the project's actual stack — R package structure for R projects, Node.js stack details for web projects, etc. Commands and paths reference the real project layout.
If fail: If the project uses an unfamiliar stack, inspect package.json, DESCRIPTION, Cargo.toml, or equivalent to identify the technology and add the corresponding section.
Step 3: Add MCP Server Information
## Available MCP Servers
### r-mcptools (R Integration)
- **Purpose**: Connect to R/RStudio sessions
- **Status**: Configured
- **Configuration**: `claude mcp add r-mcptools stdio "Rscript.exe" -- -e "mcptools::mcp_server()"`
### hf-mcp-server (Hugging Face)
- **Purpose**: AI/ML model and dataset access
- **Status**: Configured
- **Configuration**: `claude mcp add hf-mcp-server -e HF_TOKEN=token -- mcp-remote https://huggingface.co/mcp`
Got: Each configured MCP server has a subsection documenting its purpose, status (configured/available/not configured), and the command used to add it. No actual tokens or secrets are included.
If fail: If MCP servers are not yet configured, document them as "Available" with setup instructions rather than "Configured." Use placeholder values like your_token_here for any credentials.
Step 4: Add Author Information
## Author Information
### Standard Package Authorship
- **Name**: Author Name
- **Email**: [email protected]
- **ORCID**: 0000-0000-0000-0000
- **GitHub**: username
Got: Author information section includes name, email, ORCID (for academic/research projects), and GitHub username. For R packages, the format matches DESCRIPTION file requirements.
If fail: If author information is sensitive or should not be public, use the organization name instead of personal details, or omit the section entirely for internal-only projects.
Step 5: Add Security Guidelines
## Security & Confidentiality
- Never commit `.Renviron`, `.env`, or files containing tokens
- Use placeholder values in documentation: `YOUR_TOKEN_HERE`
- Environment variables for all secrets
- Git-ignored: `.Renviron`, `.env`, `credentials.json`
Got: Security section lists files that must never be committed, placeholder conventions for documentation, and confirms that .gitignore covers all sensitive files.
If fail: If unsure which files are sensitive, run grep -rn "sk-\|ghp_\|password" . to scan for exposed secrets. Any file containing real credentials should be added to .gitignore and mentioned in this section.
Step 6: Reference Skills and Guides
## Development Best Practices References
@agent-almanac/skills/write-testthat-tests/SKILL.md
@agent-almanac/skills/submit-to-cran/SKILL.md
Got: Relevant skills and guides are referenced using @ paths, giving AI assistants access to detailed procedures for common tasks in the project.
If fail: If the referenced skills or guides do not exist at the specified paths, verify the paths or remove the references. Broken @ references provide no value and may confuse the assistant.
Step 7: Add Quality and Status Information
## Quality Status
- R CMD check: 0 errors, 0 warnings, 1 note
- Test coverage: 85%
- Tests: 200+ passing
- Vignettes: 3 (rated 9/10)
Got: Quality metrics section reflects the current state of the project with accurate numbers for check results, test coverage, test count, and documentation status.
If fail: If metrics are not yet available (new project), add placeholder entries with "TBD" and update them as the project matures. Do not fabricate numbers.
Validation
- CLAUDE.md is in project root
- Quick start commands are accurate and work
- Architecture section reflects actual project structure
- No sensitive information (tokens, passwords, private paths)
- MCP server configurations are current
- Referenced files and paths exist
Pitfalls
- Stale information: Update CLAUDE.md when project structure changes
- Too much detail: Keep it concise. Link to detailed guides rather than duplicating content.
- Sensitive data: Never include actual tokens or credentials. Use placeholders.
- Conflicting instructions: Ensure CLAUDE.md doesn't contradict other config files
- Missing from
.Rbuildignore: For R packages, add^CLAUDE\\.md$to.Rbuildignore
Examples
Pattern observed across successful projects:
- putior (829 lines): Comprehensive CLAUDE.md with quality metrics, 20 accomplishments, MCP integration details, and development workflow
- Simple project (20 lines): Just quick start commands and key conventions
Scale the CLAUDE.md to match project complexity.
Related Skills
create-r-package- CLAUDE.md as part of package setupconfigure-mcp-server- MCP configuration referenced in CLAUDE.mdsecurity-audit-codebase- verify no secrets in CLAUDE.md
GitHub Repository
Related Skills
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
polymarket
MetaThis skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.
creating-opencode-plugins
MetaThis skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
