Python developer reined in Claude Code with a public anti-regression config

A developer from CREATMAN spent several weeks studying documentation, Reddit discussions, and other people's setups to assemble a Claude Code configuration with real regression protection. The result is an open repository with ready-to-use files for immediate implementation.

A Problem Everyone Knows

Claude Code works powerfully, but without proper configuration it acts too independently. A typical scenario for a solo developer:

• Asked to add authentication — the agent rewrote half the project
• Asked to fix one function — got deleted tests and an unrequested dependency
• Built a working prototype in an hour — spent three hours cleaning up the agent's "improvements"

For those working solo, this is doubly critical. There's no QA engineer to catch regressions after each prompt. There's no reviewer to notice deleted tests. Every unplanned fix is time spent parsing someone else's solutions instead of working on the product. The author runs CREATMAN without a team and faced exactly this: AI helps build fast, but without control it creates new problems just as quickly.

What the Configuration Includes

After several weeks of reading documentation, forum discussions, and analyzing other setups, the author identified five key components. All are assembled in the claude-code-antiregression-setup repository and ready to use:

• CLAUDE.md — the main contract file: task scope, explicit prohibitions on independent solutions, requirements to describe upcoming changes before executing them
• Directory rules — folder-level restrictions so the agent doesn't touch what it wasn't explicitly asked to
• Mandatory checklist — before any changes, the agent describes the plan and the developer confirms or rejects it
• Hooks — automated scripts that run after each agent action and verify it matches the task
• Prompt templates — ready-made request formats that eliminate ambiguity and narrow the space for interpretation

What's important is that everything is documented. The repository is designed not for study, but for quick deployment: clone, adapt to your project, use.

Why Contract Works Better Than Restriction

The key idea is to give the agent clear rules, not just limit it. Claude Code reads CLAUDE.md at the start of each session. If it explicitly says "don't touch tests without a request," "changes only within specified files," or "warn before adding a dependency" — the agent follows this.

"I need an AI agent to help me build faster, not create new problems,"

the author explains the goal.

Hooks add a second layer. Small scripts run after changes and check whether the agent went out of bounds. If it did — changes are marked for review instead of silently entering the codebase.

Prompt templates solve another problem. A vague request gives the agent too much space for interpretation — and that's where independent action begins. Ready-made formats with explicit scope, files, and expected results sharply narrow this space.

CLAUDE.md is not just a README for the agent, but a living project document. It fixes architectural decisions, critical modules, naming conventions. The agent uses this file as context with each request — and this makes its understanding of the project more concrete and random "improvements" less likely.

The configuration isn't tied to a specific stack. The author works with a Python backend, but the principles apply to any project where you need to keep the agent within the bounds of the given task.

What This Means

Unpredictability is one of the main barriers to adopting AI agents in solo development. Open configs like this lower the entry threshold: you take the structure, adapt it to your project, and get a manageable tool instead of a chaotic assistant. The more such setups appear in the open, the more practical it becomes to use AI agents without a support team.