Using AI Coding Agents with API Platform

AI coding agents — Claude Code, Cursor, GitHub Copilot, OpenAI Codex, Gemini, and others — are trained on snapshots of public code. Those snapshots lag behind the framework: an agent may suggest the #[ApiFilter] attribute, legacy controllers, or a serialization pattern that API Platform 4.x has since replaced. The result is plausible-looking code that does not match the current canonical way of doing things.

This guide describes two complementary ways to keep an agent on current API Platform 4.x APIs while it works in your project:

• The API Platform skillset, a Claude Code plugin that teaches Claude the canonical patterns, verified against api-platform/core.
• An agent-agnostic AGENTS.md file that points any agent at the official documentation as the source of truth.

Note: This guide covers dev-time guidance — keeping the agent that writes your code on current APIs. Exposing your running API to AI agents as callable tools is a different concern, covered by the MCP integration.

The API Platform skillset is a Claude Code plugin that ships 15 skills for API Platform 4.x development. Each skill teaches the current canonical way to do one thing, verified against api-platform/core, and covers both the Symfony and Laravel integrations.

A skill is more than a documentation link: it is a focused set of instructions, code patterns, and constraints that Claude loads on demand. When you ask Claude to add a filter, the api-filter skill loads and steers it toward QueryParameter rather than the legacy #[ApiFilter] attribute.

The plugin is distributed through a Claude Code marketplace. Add the marketplace, then install the plugin:

/plugin marketplace add api-platform/skillset
/plugin install api-platform@api-platform-skillset

These are Claude Code slash commands — run them from inside a Claude Code session, not from your shell.

Skills are namespaced as api-platform:<skill> (for example api-platform:api-filter). You do not invoke them manually: Claude loads a skill automatically when the task at hand is relevant to it. No configuration is required once the plugin is installed.

The canonical patterns evolve with each release. Update the marketplace to pull the latest skills:

/plugin marketplace update api-platform-skillset

The skillset is a Claude Code plugin: its SKILL.md plus .claude-plugin/ marketplace format is specific to Claude Code, so it does not load into other agents. For Cursor, GitHub Copilot, OpenAI Codex, Gemini, and any other agent, the portable option is an AGENTS.md file at the root of your project.

AGENTS.md is a convention — a plain Markdown file that most coding agents read for project-specific instructions. It is lighter-weight than the Claude skillset: it does not ship verified code patterns, it points the agent at where the current truth lives. The single most useful instruction is to treat the official documentation as authoritative over training data:

# AGENTS.md

This project uses [API Platform](https://api-platform.com) 4.x.

The official documentation at <https://api-platform.com/docs> is the source of truth. Prefer it over
patterns from your training data, which may describe older versions of the framework.

In particular:

- Declare collection filters with `QueryParameter`, not the legacy `#[ApiFilter]` attribute.
- Read and write logic belongs in state providers and state processors, not in custom controllers.
- Check the documentation for the current canonical pattern before generating API Platform code.

Tailor the bullet list to the conventions your project cares about. The point is to redirect the agent to current documentation rather than letting it rely on stale training data.

Claude Code also reads a CLAUDE.md file. If you maintain both an AGENTS.md and use Claude Code, keep a thin CLAUDE.md that imports the shared file rather than duplicating its content:

# CLAUDE.md

@AGENTS.md

This keeps a single source of project guidance for every agent. For Claude Code specifically, the skillset remains the richer option and complements AGENTS.md.

The documentation is also published in the llms.txt format, an agent-agnostic index any LLM can fetch to ground its answers in the current documentation:

• https://api-platform.com/docs/llms.txt — a curated index of the documentation, one link per page, grouped by chapter.
• https://api-platform.com/docs/llms-full.txt — the full text of every current-version page, concatenated.

Where AGENTS.md tells an agent how to behave, llms.txt tells it where the current reference lives. The two are complementary: point your AGENTS.md at these URLs so the agent reads the documentation instead of relying on stale training data.

This guide is about the agent that writes your code. A separate concern is letting an AI agent call your running API at runtime — searching your books, creating an order, reading a resource. That is what the Model Context Protocol (MCP) integration provides: it turns your API Platform resources into MCP tools and resources an agent can discover and invoke.

The two are independent and can be used together:

• The skillset and AGENTS.md shape the code an agent produces during development.
• MCP exposes your deployed API as callable tools at runtime.

The api-platform-mcp skill in the skillset covers writing those MCP tool definitions; see Exposing Your API to AI Agents for the full MCP reference.