# done-0/fuck-u-code

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6,830 stars · 324 forks · TypeScript · mit

## Links

- GitHub: https://github.com/Done-0/fuck-u-code
- awesome-repositories: https://awesome-repositories.com/repository/done-0-fuck-u-code.md

## Description

This project is an AI-powered code reviewer and static analysis server that identifies low-quality files and generates automated critiques. It functions as an automated quality scoring tool that evaluates source code structure and complexity through local parsing.

The system utilizes a standardized context protocol to stream analysis results to AI agents and editors. It integrates large language models to produce automated reviews and suggestions for improvement based on quantitative quality metrics.

The tool includes a weight-based scoring engine and an asynchronous analysis pipeline for processing files. It supports project-level configuration for file exclusions and metric weights, and exports analysis findings via static code analysis reporting.

## Tags

### Artificial Intelligence & ML

- [AI Code Reviewers](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-code-reviewers.md) — An AI-powered system that analyzes source code to produce automated critiques and improvement suggestions. ([source](https://cdn.jsdelivr.net/gh/done-0/fuck-u-code@main/README.md))
- [AI Agent Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-agent-integrations.md) — Connects analysis results to AI agents and editors via a standardized protocol to trigger quality checks.
- [Code Quality Scoring](https://awesome-repositories.com/f/artificial-intelligence-ml/evaluation-metrics/scoring-pipelines/feature-cross-scoring/criteria-based-scoring-engines/code-quality-scoring.md) — Calculates overall quality scores by applying configurable metric weights to various code analysis dimensions.

### Development Tools & Productivity

- [Code Quality and Analysis](https://awesome-repositories.com/f/development-tools-productivity/code-quality-analysis.md) — Evaluates source code complexity and structure through local parsing to generate quantitative quality scores.

### Networking & Communication

- [Model Context Protocols](https://awesome-repositories.com/f/networking-communication/communication-protocols-architectures/communication-protocols-standards/integration-protocols/model-context-protocols.md) — Transmits analysis findings to external agents using a standardized communication protocol for real-time updates.

### Software Engineering & Architecture

- [Abstract Syntax Tree Parsing](https://awesome-repositories.com/f/software-engineering-architecture/abstract-syntax-tree-parsing.md) — Generates abstract syntax trees locally to analyze source code structure and complexity.
- [Model Context Protocol Integrations](https://awesome-repositories.com/f/software-engineering-architecture/integration-extensibility/programmatic-interfaces/model-context-protocol-integrations.md) — Implements a standardized Model Context Protocol to stream analysis results to AI agents and editors. ([source](https://cdn.jsdelivr.net/gh/done-0/fuck-u-code@main/README.md))
- [Asynchronous Processing Pipelines](https://awesome-repositories.com/f/software-engineering-architecture/asynchronous-processing-pipelines.md) — Utilizes a concurrency-controlled queue to process multiple files in parallel via an asynchronous pipeline.
- [Project Configuration Management](https://awesome-repositories.com/f/software-engineering-architecture/project-configuration-management.md) — Provides project-level management for file exclusions, concurrency settings, and metric weights. ([source](https://cdn.jsdelivr.net/gh/done-0/fuck-u-code@main/README.md))

### Testing & Quality Assurance

- [Code Quality and Review](https://awesome-repositories.com/f/testing-quality-assurance/code-quality-review.md) — Uses large language models to perform heuristic reviews and generate automated code quality critiques.
- [Code Quality Tools](https://awesome-repositories.com/f/testing-quality-assurance/code-quality-review/code-quality-tools.md) — Measures code quality using local parsing and weighted metrics with customizable project rules.
- [LLM Analyzers](https://awesome-repositories.com/f/testing-quality-assurance/code-quality-review/code-quality-tools/llm-analyzers.md) — Uses large language models to critique source code and suggest improvements based on quality metrics.
- [Static Code Analysis](https://awesome-repositories.com/f/testing-quality-assurance/static-code-analysis.md) — Acts as a server that parses source code to evaluate complexity and structure for AI agent consumption.
- [Static Analysis Exporters](https://awesome-repositories.com/f/testing-quality-assurance/general-testing-utilities/test-reporters/external-result-reporters/tap-result-exporters/test-report-exporters/static-analysis-exporters.md) — Exports quantitative quality metrics and review results in standardized formats for external reporting.