Codebase intelligence for AI and humans: code health scores, auto-generated docs, git analytics, dead code detection, and architectural decisions via MCP.

An Intelligent Python Code Quality Analyzer

An extension for flake8 that validates cognitive functions complexity

Technical debt and risk analyzer that predicts bug hotspots by combining cognitive complexity, pattern recognition, coverage gaps, information theory, and git history.

🦎Official fastlane plugin for lizard code complexity analytics 🦎

🧩 Análise em tempo real da complexidade assintótica de Tempo (Big O) e Espaço (Big O) para VS Code e editores compatíveis.

🔍 CodeMetrix: A sophisticated code analysis and cost estimation tool that provides advanced metrics, quality assessment, and intelligent reporting for software projects. Features COCOMO II modeling, AST-based analysis, and multi-language support.

Measure the complexity of TodoMVC implementations

Neovim code intelligence plugin for exploring function relationships

A pragmatic tool for measuring whether a codebase is too complex for its task.

Static analysis of Shopsys, Sylius and Spryker

Educational library in Javascript that is great for kids to learn programming. This library emulates a "console" with simple println and input command.

Replication package for "Not All AI Code Is Equal" — a multi-model comparison of defects, vulnerabilities, and complexity in human- and AI-generated code. Runs Pylint/PMD, Semgrep, and Lizard analyses over 500 Python & Java tasks each from Claude Opus 4.8, Gemini 2.5 Pro, and Qwen2.5-Coder vs. human code.

CLI for arborist-metrics — code complexity analysis from the command line

This repository allows the replication of our study "Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity" accepted for publication at The 36th IEEE International Symposium on Software Reliability Engineering (ISSRE 2025).

A simple tool to analyzing and visualizing code complexity metrics in go.

Measure your codebase from the terminal — lines per language, duplicate code, complexity, token cost for AI agents, comment drift, change coupling, a refactor verdict per file, and what is safe to hand to an agent. JSON output, CI exit codes, PR diff mode and an MCP server. One dependency, 100% test coverage.

Codebase analysis for TypeScript, JavaScript, and Python. Dead files, unused exports/imports/deps, cycles, complexity, and health grades — monorepo-aware, zero-config, deterministic Rust CLI. CI baseline ratchet, framework plugins, JSON/SARIF for agents. No tsc. No network.

Phase 1 of the Big-O code runtime complexity prediction capstone (Capstone B-10).