AI Quick Start
If you want to go through Module Federation concepts, configuration, and integration in order, head to the Getting Started guide.
A Better Way To Start
If you are already using an AI coding tool, the better starting point is not to read all of the docs yourself first.
The faster approach is: give the latest Module Federation docs to your agent first, then let it answer questions, debug problems, update config, and write code.
Many models still have incomplete or outdated knowledge of Module Federation 2.0. So the point of this page is not to make you study first. It is to help your agent get the right source material first.
Recommended Setup
If you use Claude Code, Cursor, Windsurf, or another editor with Skills support, install:
Then ask the agent to read the docs before answering:
The idea is simple:
- you do not need to hunt through the docs first
- your agent reads the latest docs before it answers
- the result is much more likely to be accurate
If You Want Your Agent To Do More
The mf skill does more than answer questions. It can also help your agent:
- add Module Federation to an existing project
- inspect config problems
- analyze type issues
- check shared dependency conflicts
- inspect remote module information
- use the Divebell MF Extension to diagnose runtime failures, loading chains, and performance bottlenecks
- read application observability reports from local, SSR, or production environments
👉 View Agent Skills documentation
Observe and Debug MF Loading
Remote resolution, Shared selection, Bridge lifecycle, and module performance
problems often exist only in the live page runtime. For a one-off diagnosis,
the mf skill can use the Divebell MF Extension
to obtain bounded, structured evidence without adding the Observability Plugin
to the application. Page interaction is used only to trigger the loading path.
Ask the agent directly:
When needed, the skill installs Divebell globally on the agent machine, reads
the CLI Skill returned by divebell skill, installs
@divebell/extension-mf, and reads the command Skill returned by
divebell mf --skill. Neither package is added to your application.
For continuous collection, Node/SSR files, production uploads, or reports that
must be retained, integrate the
Observability Plugin. After a
loading failure, give the generated traceId, read: command, or uploaded
report to the agent:
For Node or SSR, ask the agent to read .mf/observability/latest.json. For
production, upload reports with the plugin's onReport callback and give the
stored report to your agent. A trace ID from a closed browser session is not a
substitute for the stored report.
You Can Still Use This Without Installing Anything
If you just want a lightweight version first, send this directly to your agent:
This is the easiest way to stop the model from guessing when you have not installed the skill yet.
Docs You Can Hand To Your Agent
llms.txt
module-federation.io supports the llms.txt standard, which is a good entry point for AI systems:
Markdown Docs
Every page on the docs site is also available in Markdown. Replace .html at the end of a docs URL with .md:
Next
If you want to start using this workflow right away:
- Install
mf - Ask the question you care about most
- Use Divebell for one-off MF runtime, loading-chain, or performance diagnosis
- Enable the Observability Plugin when loading evidence must be retained or uploaded
- Open Skills documentation to see which sub-commands it supports