Using Coding Agents

Hatchet is designed to work well with AI coding agents. This page covers how to give your agent access to Hatchet documentation and step-by-step skills for common CLI operations.

Prerequisite: The hatchet skills install and hatchet docs install commands require the Hatchet CLI. See the CLI reference for installation instructions.

Agent Skills

Agent skills are reference documents that teach AI coding agents how to use the Hatchet CLI — triggering workflows, starting workers, debugging runs, and more.

Run the following command in your project root to install the skill package:

hatchet skills install

This creates a skills/hatchet-cli/ directory with step-by-step reference files and appends a section to your project's AGENTS.md (and CLAUDE.md) pointing agents to the right file for each task.

Install to a custom directory:

hatchet skills install --dir ./my-project

After installation, commit the skills/ directory and AGENTS.md to version control so all agents working in the repo benefit automatically.

Available references

Reference	When to use
`references/setup-cli.md`	Installing the CLI, creating or listing profiles
`references/local-dev-embedded.md`	Running Hatchet locally with no token, server, or Docker
`references/start-worker.md`	Starting a dev worker for local development
`references/trigger-and-watch.md`	Triggering a workflow and polling for completion
`references/debug-run.md`	Diagnosing a failed, stuck, or unexpected run
`references/replay-run.md`	Re-running a previous workflow with same or new input

Local Development with Embedded Mode

For local development, an agent does not need an API token or Docker. Embedded mode runs a full Hatchet engine locally, so an agent can start a worker, trigger tasks, and inspect results without any account setup.

Each SDK has an embedded entry point: Python uses Hatchet.from_embedded(), TypeScript uses HatchetEmbeddedClient.init() from @hatchet-dev/typescript-sdk/v1/embedded, and Go uses hatchet.WithEmbedded() with a blank import of github.com/hatchet-dev/hatchet-embedded:

examples/python/embedded/worker.py

hatchet = Hatchet.from_embedded()

hatchet = Hatchet.from_embedded()

See Embedded Mode for configuration options, stopping the engine, and serving the dashboard.

MCP Server

Hatchet documentation is available as an MCP (Model Context Protocol) server, so AI coding assistants like Cursor and Claude Code can search and reference Hatchet docs directly.

MCP endpoint: https://docs.hatchet.run/api/mcp

hatchet docs install cursor

This creates a .cursor/rules/hatchet-docs.mdc file and prints the one-click deeplink.

llms.txt

For any AI tool that supports llms.txt, Hatchet docs are available at:

Resource	URL
llms.txt (index)	docs.hatchet.run/llms.txt
llms-full.txt (all docs)	docs.hatchet.run/llms-full.txt
Per-page markdown	`docs.hatchet.run/llms/{section}/{page}.md`
MCP endpoint	`https://docs.hatchet.run/api/mcp`

Every documentation page also includes a <link rel="alternate" type="text/markdown"> header pointing to its markdown version, and a "View as Markdown" link at the top of the page.

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