AI that evaluates and critiques your codebase to detect typical errors generated by AI.
roast-my-code is a command-line tool that analyzes your repository to identify bad practices left by AI coding assistants: TODOs, generic variable names (foo/bar/temp), empty exception handling, commented-out blocks, etc. It assigns a score out of 100 and uses an LLM (Groq, free) to generate a personalized critique, citing your files and the worst errors. Unlike pylint or flake8, it specifically targets traces left by AI. Generates a shareable HTML report and a badge.
For computer science students, roast-my-code helps identify common AI-generated code smells. It can analyze student projects to pinpoint issues like generic variable names or commented-out code, providing specific feedback for improvement. For example, a student could use it to find 'foo' and 'bar' variables in their assignment, leading to more readable code.
For independent developers, roast-my-code offers a quick quality check on code produced with AI assistance. It can scan a personal project's repository to highlight potential issues before deployment, ensuring a cleaner codebase. For instance, a solopreneur could use it to find empty exception blocks in their app, preventing unexpected runtime errors.
For open-source project maintainers, roast-my-code can be integrated to automatically review pull requests for AI-introduced anti-patterns. It provides a consistent, automated critique of code quality, flagging common AI-generated issues like excessive TODO comments. This helps maintainers focus on architectural reviews rather than basic code hygiene.
For software engineers using AI coding assistants, roast-my-code provides a targeted review of generated code. It can analyze a specific file or function to identify and report on AI-specific bad practices, such as placeholder variable names. This allows developers to quickly refine AI outputs into production-ready code.
RoastMyCode.ai offers a free tier that allows for one public repository analysis. This includes a full architecture review, risk assessment, and contributor roasts. Paid plans are available for more analyses and private repositories.
RoastMyCode.ai offers pay-per-analysis credits. A single credit costs $0 for a free public repository analysis. Paid options include 3 credits for $12 and 10 credits for $29, with credits never expiring.
RoastMyCode.ai states that code is not stored; repositories are cloned into ephemeral environments that are destroyed after analysis. They use AI providers with zero data retention policies, and code is not used to train AI models. Private repositories require explicit OAuth authorization, and users can revoke access and delete reports.
While RoastMyCode.ai focuses on AI-generated code artifacts, general code analysis tools like SonarQube, Codacy, and DeepSource offer broader code quality and security checks. These platforms provide static code analysis, bug detection, and security vulnerability scanning across various programming languages.
RoastMyCode.ai is a web-based service accessible through a browser, and it also offers a GitHub App for automated pull request reviews. There is no standalone mobile or desktop application mentioned.
RoastMyCode.ai can be used directly through their website by pasting a GitHub URL or by installing their GitHub App for automated pull request reviews. The command-line tool aspect mentioned in its description suggests a potential local installation, but the primary user-facing methods are web-based.
RoastMyCode.ai is designed to analyze codebases for typical errors and bad practices left by AI coding assistants. This includes identifying TODOs, generic variable names like 'foo' or 'temp', empty exception handling, and commented-out code blocks.
We're still verifying the official pricing for roast-my-code. In the meantime, the most up-to-date plans and prices are available directly on the publisher's website.
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