The Indie Founder Codebase Health Audit Playbook
Catch drift, debt, and security gaps before your users do
For solo founders and indie hackers who have been vibe-coding or shipping fast and need to know how bad things really are under the hood. This playbook walks you through scanning, documenting, and hardening your codebase without a senior engineer or DevOps team. It's the difference between shipping confidently and waking up to a production outage.
Goal
Audit, document, and harden an existing codebase for production readiness
Who this is for
Indie hackers and solo founders sitting on fast-shipped or AI-generated codebases
When to use
Before a public launch, investor demo, or when onboarding your first paying customers
When NOT to use
If you are still in early prototyping and nothing is live yet
How to set it up
Scan for architectural drift and security gaps
Run VibeDrift against your entire repository. Prioritise the issues it flags by severity — focus on security and architectural gaps first, cosmetic drift second.
Generate living specs from your codebase
Point Specsight at your repo to auto-generate product specs. Save these as your canonical reference document — update them every time you ship a meaningful change.
Generate full codebase documentation
Use the codebase docs generator to produce module-level documentation. Commit this to your repo so AI coding tools always have accurate context in every session.
Add a firewall to every LLM API call
Install Senthex with one line of code around your LLM endpoints. Verify it blocks common injection patterns in your staging environment before pushing to production.
Review and merge hardening PRs as readable chapters
Submit your fixes as pull requests and use Stage to organise them into logical review chapters. This keeps your diff readable and gives you a clear audit trail of every hardening decision.
Scan AI-generated codebases for architectural drift and security gaps
Pinpoints exactly where your AI-generated code has diverged from sound architecture or introduced security gaps, so you know what to fix first.
Auto-generate living product specs from your codebase for PMs and stakeholders
Auto-generates up-to-date product specs from your actual codebase so you and any AI tools always have accurate context about what the system does.
Auto-generate codebase documentation for AI agents and developers
Produces readable documentation for every module so AI agents and future-you can navigate the codebase without guessing.
Protect LLM API calls with sub-16ms security layer
Drops a sub-16ms security firewall around every LLM call in your app so prompt injection and abuse vectors are closed before users find them.
AI code review that organizes pull requests into logical chapters for clarity
Organises your hardening pull requests into logical chapters so you can review changes clearly without losing track of what each fix actually does.
Expected outcome
A fully documented, drift-scanned, and hardened codebase with living specs and a security layer on your LLM calls
Related playbooks
The Indie Founder Agentic QA Hardening Playbook
Ship AI-generated code to production with confidence and zero critical regressions
The Indie Founder Legacy Code Modernisation Playbook
Understand, document, and modernise a legacy codebase as a solo founder
The Indie Founder Codebase Archaeology Playbook
Turn an unknown, undocumented codebase into something you can confidently ship and extend
The Indie Founder SaaS Architecture Clarity Playbook
Turn a system idea into a documented, cost-estimated, reviewable architecture
Was this playbook useful?
This playbook is a curated starting point, not a definitive recommendation. Pricing and features change — always verify on each tool's official website. Tools marked "affiliate link" may earn this site a commission at no extra cost to you.