Give every engineer your team's best AI setup.

FML audits how engineers have configured their agents, finds missing skills and context sources, and helps turn strong local workflows into shared team assets.

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Practice library

Skills, commands, hooks, MCP servers, agents, rules, and instructions.

All 42Skills 9Commands 4Hooks 6MCP 3

api-contract

Skill

Created from repeated checkout sessions

7 active / 12 installed

test-plan

Command

Used before edits in auth and billing

19 active / 24 installed

sentry-context

MCP

Configured for production bug sessions

4 active / 9 installed

review-checklist

Hook

Turns repeated review feedback into rules

3 active / 8 installed

Setup audit

Skills installed but unused

good candidates for training or cleanup

5

MCP configured but idle

Sentry and PostHog not called in sessions

3

Missing repo instructions

teams starting without local project rules

4

See how your team actually uses AI

The local FML plugin builds a report around session history, skill use, workflows, and other inputs—across every harness your team uses.

Session history

Prompts, tool calls, files read, files edited, tests run, and the places where agents loop.

Setup snapshots

Skills, commands, hooks, MCP servers, models, and permissions.

Workflows

Repeated successful workflows and review feedback that should become reusable across the team.

How teams use it

Turn "one engineer has a great setup" into a shared library for the entire company.

01

Audit the setup

Compare skills, commands, hooks, MCP servers, agents, rules, and instructions across the team.

02

Create the missing skills

Turn repeated successful sessions and review feedback into skills or hooks engineers can install.

03

Track whether people use them

Measure active versus installed usage so a published skill does not get mistaken for real adoption.

Find the skills and setup gaps blocking wider AI use.

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