ray-project--ray/doc/source/ray-contribute/agent-development.md

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html_meta description How to configure and use AI coding agents like Claude Code on the Ray codebase, including the repository's shared CLAUDE.md instructions, rules, skills, and personal environment setup. Read this to work effectively with AI coding agents when developing Ray.

(agent-development)=

Using agents for development

AI coding agents can accelerate development on the Ray codebase. This guide covers how the Ray project is configured for agent-assisted development and how to set up your local environment.

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(claude-code-setup)=

Claude Code

Claude Code is an AI coding assistant that understands the Ray codebase through a hierarchy of instruction files, rules, and skills. For installation instructions, see the official documentation.

Project configuration

The Ray repository includes shared Claude Code configuration that is version-controlled:

• .claude/CLAUDE.md: root instructions loaded in every session
• <library>/.claude/CLAUDE.md: library-specific instructions loaded on-demand (for example, python/ray/data/.claude/CLAUDE.md)
• .claude/rules/: coding rules scoped by file type
• .claude/skills/: reusable workflows (rebuild, lint, fetch CI logs)
• .claude/agents/: project-specific subagents

Personal configuration lives in files that are not version-controlled:

• CLAUDE.local.md: your environment-specific instructions
• .claude/settings.local.json: your personal permission overrides

Personal setup

After installing Claude Code, create a CLAUDE.local.md file in the repository root with your environment-specific configuration:

## My Environment
- Python: /path/to/your/python
- Test runner: /path/to/your/python -m pytest

## My Git Setup
- origin = your-username/ray (fork)
- upstream = ray-project/ray (main repo)

## Preferences
- Add any personal preferences here

This file is gitignored and isn't committed.

Cross-worktree setup

If you use multiple git worktrees, CLAUDE.local.md only exists in the worktree where you created it. To automatically symlink it from your main checkout whenever a new worktree is created, set up a post-checkout git hook:

1. From your main Ray checkout (not a worktree), create the hook file at $(git rev-parse --git-common-dir)/hooks/post-checkout with the following contents:

   #!/bin/bash
   # Auto-symlink CLAUDE.local.md into new worktrees.
   MAIN_REPO="$(git rev-parse --git-common-dir)/.."
   MAIN_LOCAL_MD="$(cd "$MAIN_REPO" && pwd)/CLAUDE.local.md"
   if [ -f "$MAIN_LOCAL_MD" ] && [ ! -e "CLAUDE.local.md" ]; then
       ln -s "$MAIN_LOCAL_MD" CLAUDE.local.md
   fi
   

2. Make it executable:

   chmod +x "$(git rev-parse --git-common-dir)/hooks/post-checkout"
   

3. The hook fires automatically when you create a new worktree with git worktree add. For existing worktrees, run the symlink manually:

   ln -s /path/to/ray/CLAUDE.local.md CLAUDE.local.md
   

Buildkite token setup

The /fetch-buildkite-logs skill requires a Buildkite API token to fetch CI logs.

1. Go to https://buildkite.com/user/api-access-tokens

2. Create a new token with these scopes:

   • read_builds
   • read_build_logs

3. Add it to your shell profile:

   # Add to ~/.bashrc or ~/.zshrc
   export BUILDKITE_API_TOKEN="your-token-here"
   

4. Reload your shell: source ~/.bashrc

Available skills

Shared skills available in every session:

• /rebuild: guided Ray rebuild based on what files changed
• /lint: run linting and formatting checks
• /fetch-buildkite-logs: fetch and analyze Buildkite CI logs

Adding team rules

Each Ray library has a .claude/rules/ directory where teams can add coding rules that apply when working on their files. To add a new rule:

1. Create a .md file in your library's rules directory, for example, python/ray/data/.claude/rules/data-conventions.md

2. Add a paths frontmatter to scope it to your files:

   ---
   paths:
     - "python/ray/data/**/*.py"
   ---
   - Use logical operators from ray.data._internal.logical.operators
   - Prefer streaming execution over batch where possible
   

Rules without paths frontmatter load unconditionally in every session. See the README.md in each rules directory for examples.

Adding team skills

Skills are reusable workflows that load on-demand when invoked with /<skill-name>. To add a new skill:

1. Create a directory under your library's .claude/skills/, for example, python/ray/data/.claude/skills/debug-data/

2. Add a SKILL.md file with frontmatter:

   ---
   name: debug-data
   description: Debug Ray Data pipeline issues
   ---
   
   # Debug Data Pipeline
   
   ## Steps
   1. Check the Data execution plan...
   2. Look for common issues...
   

Skills in a library's .claude/skills/ directory are discovered when working in that library. Shared skills in .claude/skills/ are available everywhere.