Build Your AI Context System
Answer 3 short questions and get a personalized CLAUDE.md starter kit, a maturity level assessment, and concrete next steps.
Generate Your Starter Kit
Tell us about your team size, what you already have set up, and your tech stack. We will generate a ready-to-use CLAUDE.md and score your context maturity level.
Team size
How many developers work on this project?
AI tools you use (select all that apply)
Which AI coding tools are active in your workflow?
Your CLAUDE.md status
How developed is your current context file?
Separate rules files
How many .md files in your rules/ or .claude/ directories?
Primary language
What is the main backend or system language?
Frontend framework
Any frontend layer on this project?
Your Starter Kit
Context Maturity
Level 1 — Starter
Current criteria
Next steps to level up
What Is Context Engineering?
The practice of systematically designing and managing the information you give AI coding tools — so they produce better output, more reliably.
CLAUDE.md as a contract
The CLAUDE.md file tells Claude Code what your project is, how it is structured, and what the team's conventions are. It is read at the start of every session, so it shapes every response.
Modular rules files
Instead of one giant file, experienced teams extract rules into separate .md files — one per concern (security, testing, git) — and import them via @path syntax. Easier to maintain, easier to scope by directory.
Profile-based assembly
On larger teams, different roles need different context. A profile YAML defines which modules to assemble for a given team member — frontend dev, backend dev, or staff engineer.
The ACE pipeline
Assemble, Check, Execute. The most mature setups automate context assembly from modules, run drift detection in CI to catch stale rules, and feed session learnings back into the context system.
The 5 Maturity Levels
Where most teams start and where the best teams end up.
Starter
Single CLAUDE.md, written once, rarely updated. Better than nothing, but drift accumulates fast.
Modular
Rules extracted to separate files, organized by concern. @imports keep the root file clean. Path-scoping directs rules to the right subdirectories.
Team-Ready
Profile-based assembly for different roles. Shared module library across the team. CI drift detection flags when docs fall out of sync with the codebase.
Measured
Quarterly self-evaluations test whether Claude follows the rules. Canary checks in CI verify rule adherence. Metrics per rule, not just per project.
Adaptive
Knowledge feeding loop: every session retrospective updates the context. ACE pipeline runs automatically. The context system improves itself over time.
Full guide in the docs
The Context Engineering section covers every maturity level in depth — architecture patterns, example file structures, CI setup, and real team configurations.