Can Vibecheck be integrated into CI/CD pipelines?

Yes, Vibecheck is CI/CD native. It supports JSON output for machine parsing, non-zero exit codes for build failures, and direct GitHub Actions annotations for seamless integration into your workflows.

What is Vibecheck and what does it do?

Vibecheck is a high-performance, featherweight Zig CLI tool that performs static analysis to detect code smells, secrets, debug prints, and incomplete development tasks (like 'FIX ME') in your codebase.

How fast and efficient is Vibecheck?

It's blazing-fast, scanning thousands of files in milliseconds. It's also incredibly lightweight, a ~149KB static binary with zero dependencies, making it highly portable and efficient.

Is it possible to customize what Vibecheck scans for?

Absolutely. Vibecheck is modular, allowing you to load custom pattern packs (called 'Vibe Packs') via a JSON plugin system to define and scan for your own tailored patterns and issues.

Vibecheck

Name: Vibecheck
Author: copyleftdev

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Performs high-performance static analysis to identify code smells, secrets, debug prints, and incomplete development tasks in codebases.

Vibecheck is a blazing-fast, featherweight (149KB) static analysis CLI tool written in Zig, designed to scan your codebase for 'unfinished vibes' such as To-Dos, hardcoded secrets, debug statements, and other common code smells. It's built for speed, scanning thousands of files in milliseconds, and is highly portable with a single executable across Linux, Mac, and Windows. Ideal for integration into CI/CD pipelines, it offers machine-readable JSON output, non-zero exit codes for failures, and GitHub Actions annotations. Additionally, it features a built-in Model Context Protocol (MCP) Server, making it ready for use by AI agents to enhance code quality workflows.

主要功能

01Featherweight & Portable: ~149KB static binary, zero dependencies, runs cross-platform.

02Modular: Load custom pattern packs via a JSON plugin system for tailored analysis.

03CI/CD Native: Supports JSON output, non-zero exit codes, and GitHub Actions annotations.

04AI Ready: Built-in Model Context Protocol (MCP) Server for AI agent integration.

051 GitHub stars

06Blazing Fast: Recursively scans thousands of files in milliseconds.

使用案例

01Quickly scanning local development branches for immediate feedback on code smells and potential issues.

02Automating code quality checks in CI/CD pipelines to prevent 'unfinished vibes' from merging.

03Providing AI agents with contextual information on codebase quality to assist with refactoring, bug fixing, or code generation tasks.