An engineer, an AI robot, and a business professional kneel around a large unfolded map, tracing routes together

Map

Chart the territory before you move through it. Map lets you describe your engineering competencies — skills, behaviours, grades, disciplines, and tracks — in plain YAML files that humans can read and machines can validate.

Before you can chart a career path or deploy an agent team, you need to define the landscape. Map is the foundational data model — the engineering skills taxonomy that everything else references. Define it once in YAML, and the rest of the system derives from it automatically.

What you get


Who it's for

Engineering leaders who want to codify what "good" looks like across their organization. Define it once in YAML, and the rest of the system — job descriptions, agent profiles, interview questions — derives from it.

Platform teams building internal developer tools. Map provides the structured data foundation that other apps consume.


How Data is Organized

All definitions live in YAML files under your data directory:

data/
├── grades.yaml           # Career levels (L1–L5)
├── stages.yaml           # Engineering lifecycle phases
├── drivers.yaml          # Organizational outcomes
├── disciplines/          # Engineering specialties
├── tracks/               # Work context modifiers
├── behaviours/           # Approaches to work
├── capabilities/         # Skill groups with responsibilities
└── questions/            # Interview questions

Every entity supports both human and agent perspectives in the same file — a skill definition includes human-readable level descriptions alongside agent-specific instructions for AI coding assistants.


Quick Start

Validate your data to make sure everything is connected:

npx fit-map validate

Browse what's defined:

npx fit-pathway skill --list       # All skills
npx fit-pathway discipline --list  # Engineering specialties
npx fit-pathway grade --list       # Career levels