AGENTS.md Pattern for React Native: Claude Code Generates Better Project-Aware Code
A Reddit user, julia_ships, has been experimenting with an AGENTS.md file for React Native/Expo projects. The file acts as a structured document that tells AI coding agents like Claude Code and Cursor about project-specific conventions. The difference is significant — before using the file, Claude generated generic React Native code; after, it generates components using the project's exact theme tokens, hooks, and patterns.
What Goes Into the AGENTS.md File
The user includes the following sections in their AGENTS.md:
- Folder structure and file naming conventions — e.g., where components live, how files are named (PascalCase, kebab-case, etc.)
- Theme system and color tokens — exact token names and how to apply them
- Custom hooks and their usage patterns — how to import and use project-specific hooks
- Component patterns — functional components with typed props (TypeScript), structure, and export style
Results
With the AGENTS.md in place, Claude Code produces code that matches the project's conventions on the first try. For example, it uses the correct theme colors.primary instead of hardcoded '#6200ee', imports custom hooks like useAuth() instead of writing inline logic, and follows the established component pattern (functional, typed props, proper export).
Who It's For
React Native / Expo developers using Claude Code, Cursor, or similar AI coding agents who want to reduce manual code review and force the AI to respect project conventions.
📖 Read the full source: r/ClaudeAI
👀 See Also
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Claude CLI v2.1.154 Breaks Local vLLM — One-Line Patch Fixes It
Claude CLI ≥2.1.154 adds three new API roles (ctx, msg, system) that break local vLLM compatibility. A one-line patch to vLLM's Anthropic protocol restores it.
Agent Framework Token Bloat: A 500:1 Input-to-Output Ratio Is Normal
A self-hosted agent framework user reports ~21k input tokens per message and 500:1 input-to-output ratio from tool definitions, system prompt, and memory. Community confirms 15-25k baseline context is common for tool-using agents.
5 Patterns for Getting Better Results from Claude (Non-Technical Users)
Practical scaffolding, example-based prompting, negative instructions, persistent context, and source grounding — five patterns that consistently improve output quality from Claude, backed by six months of field experience.