LetMeCheck.ai

Every repository gets a single number that sums up its engineering quality, much like a credit score or a lab result. The dashboard breaks it into sub-scores for maintainability, security risk, technical debt, and AI slop. A score above 80 percent reads as healthy. Anything below 60 percent flags work worth doing before it slows a team or causes an outage.

This is the feature the team treats as its signature. AI coding tools often produce code that works but sits poorly in the wider project, and the damage rarely shows up in a test run. LetMeCheck.ai hunts for telltale patterns: needless abstraction layers, duplicated business logic, fake enterprise scaffolding, bloated service layers, architecture drift, and dead utilities. One example insight flags a repo carrying eleven unnecessary abstraction layers around plain CRUD operations, a sign of complexity that does nothing for the product and everything to slow the next developer who touches it.

The scan runs on the same class of tools professional developers use daily. It catches bugs, code smells, duplicated code, security hotspots, complexity issues, and coverage gaps. Because the engine runs in a one-click cloud container, you skip the setup and maintenance that a self-hosted SonarQube install demands.

For $5, the Deep Dive Report expands each major issue into a full breakdown. You get a plain explanation, a root cause, a risk analysis of what breaks if you ignore it, a priority order, and an effort estimate in hours. The aim is simple. Turn a wall of warnings into a short, ranked to-do list you can work through in order.

The Recovery Pack is the premium layer, closer to a treatment plan than a diagnosis. It bundles fix recommendations with reasoning, step-by-step refactor plans, best practices fitted to your stack, and ready-to-use prompts for Claude Code, Cursor, or Devin. No more staring at a list of problems. You get something you can hand straight to your team.

Skill Files are AI prompts written for your specific issues. Each one spells out what the issue is, the relevant code snippet, the rules to follow during the fix, and the steps that keep existing behaviour intact. You feed the brief to your AI coding agent. It doesn't have to guess.