Twenty engineers use Claude Code with wildly different results. What gives you the biggest team-wide gain for the least effort?
Why A is the answer
A shared project configuration with conventions, rules and common workflows
Shared configuration travels with the repo and applies to everyone by default, so the improvement does not depend on each person remembering training.
In detail
- Shared configuration travels with the repository.
- It applies to everyone by default, so the gain does not depend on memory or training.
- Review the shared config like code.
Use case
An illustrative scenario, not a real incident.
Twenty engineers get uneven results. One repository config with conventions, rules and workflows lifts everyone, and new hires get it on day one.
See it in different ways
flowchart LR A["Shared CLAUDE.md and skills"] --> B["Committed to the repo"] B --> C["Reviewed like code"] C --> D["Every engineer gets it"]
mindmap
root((Repository))
CLAUDE.md
Conventions
Skills
Common workflows
Settings
Shared rulesflowchart LR S0["Agree conventions"] --> S1["Commit the config"] --> S2["Everyone pulls"] --> S3["Results converge"]
Key point: Shared configuration scales better than training.
Why the other options are wrong
- B. Mandatory weekly prompt writing workshops for all twenty engineers
- Workshops cost more time, and the benefit fades without shared defaults.
- C. A rule that everyone must use the same model and the same settings
- Identical models do not give people the same conventions or workflows.
- D. A dashboard ranking the engineers by token usage and merged pull requests
- Ranking by tokens rewards activity, not results.