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Fuck-U-Code

Community
Let bad code have nowhere to hide – assess the legacy-mess level of your code and output a beautiful report.
GitHub source repository ↗
★ 7.3k Stars Category · Dev Tools Very popular Source revision 420c6e803900
45FMRS · D
Reliability
5/20
Security and permissions
7/20
Maintenance
10/20
Documentation
11/20
Setup experience
12/20

fuck-u-code is a feature-rich code quality analysis tool with multi-language and AI review support. It is well-suited for developers who enjoy a humorous tone and want quick insights into code quality. However, privacy risks from AI review and its informal nature may not fit all teams.

Read the FMRS scoring method →

fuck-u-code is a code quality analysis tool designed to expose shitty code quality with sharp but humorous feedback. It uses AST parsing via tree-sitter for accurate syntax analysis, supports 14 programming languages, and performs seven quality checks: complexity, size, comments, error handling, naming, duplication, and structure. Provides an overall score from 0 to 100 (higher is better) and a per-file Shit-Gas Index (higher is worse). Supports AI code review with OpenAI-compatible, Anthropic, DeepSeek, Gemini, and Ollama providers, multiple output formats (colored terminal, Markdown, JSON, HTML), and i18n (English, Chinese, Russian). As an MCP server, it can be invoked by AI tools like Claude Code, Cursor, Windsurf, etc. Code analysis runs fully offline, so your code never leaves your machine.

Tools

analyze
Analyze code quality and generate a score report.
ai-review
Run AI-powered code review on the worst-scoring files.

Setup

Install globally: npm install -g eff-u-code. Then configure MCP automatically: fuck-u-code mcp-install (interactive) or specify client: fuck-u-code mcp-install claude / cursor. For manual configuration, add an mcpServers entry to your client config file. For example, in Claude Code's ~/.claude.json or project .mcp.json, add {"mcpServers":{"fuck-u-code":{"command":"fuck-u-code-mcp"}}}. Alternatively, you can use npx: set command to "npx" and args to ["-y", "eff-u-code-mcp"].

claude_desktop_config.json
{
  "mcpServers": {
    "fuck-u-code": {
      "command": "fuck-u-code-mcp"
    }
  }
}

Fit and risk

Best for

  • Developers and teams wanting a quick snapshot of their codebase health.
  • Users of AI coding assistants (like Claude Code) who want automated code review.
  • Projects needing multi-language support (14 languages).
  • Developers who appreciate witty and blunt feedback.

Not for

  • Teams requiring serious, professional, and emotionless code reviews.
  • Enterprise users needing deeply customizable analysis rules (e.g., custom metrics).
  • Organizations managing large-scale codebases that need more powerful enterprise tools.
  • Environments where profanity or informal tone is inappropriate.

Required permissions

  • File system access to read code files for analysis.
  • Optional: Access to external AI APIs (e.g., OpenAI, Anthropic) for AI review.
  • Write access to configuration files (~/.fuckucoderc.json) and possibly MCP config files (e.g., .mcp.json).
  • Network access for updates or external API calls.

Risks and side effects

  • AI review features send code snippets to external services (e.g., OpenAI), which may raise privacy concerns.
  • Analysis results may include false positives or missed issues, so should not be the sole quality metric.
  • Tool name and feedback style may be unprofessional for formal settings.
  • Misconfiguration can prevent the MCP server from starting.

Troubleshooting

  1. Ensure eff-u-code is installed globally (npm install -g eff-u-code).
  2. Run `fuck-u-code --version` to verify installation.
  3. Run `fuck-u-code mcp-install` to reconfigure the MCP server.
  4. Check that the configuration file (~/.fuckucoderc.json) has no syntax errors.
  5. Try the npx approach: set command to "npx" and args to ["-y", "eff-u-code-mcp"].
  6. Use `fuck-u-code config show` to view current configuration.

Use cases

Quickly assess code quality across a repository to identify files needing improvement.
Integrate into CI to automatically generate code quality reports.
Invoke from IDE or AI tools for on-the-fly code review.
Use AI models to perform in-depth review of worst-scoring files and get improvement suggestions.

Supported clients

Claude DesktopFull support