An AI skill that automatically and accurately configures linters from coding standards and enforces code quality across multiple platforms (Claude Code, Cursor, Windsurf, GitHub Copilot, etc.).

LintConfig

An AI skill that automatically interprets coding standards, configures static analysis and linting tools, executes lint checks, and assists with code quality improvement. The skill supports both configuration-only workflows (e.g., "generate a linter config for a coding standard") and end-to-end quality enforcement workflows (e.g., "check this code against our coding standard and suggest fixes").

Features

• Automatic Coding Standard Parsing: Converts natural language coding standards into formalized coding rules using a structured grammar.
• Intelligent Linter Configuration: Automatically maps coding rules to appropriate linter rules and generates validated configuration files.
• Multi-Linter Support: Currently supports Checkstyle (Java) with extensible architecture for additional linters.
• Coverage Analysis: Classifies configuration coverage (Exact Match, Over-Approximation, Under-Approximation) to help you understand how well the linter configuration aligns with your coding standards.
• End-to-End Workflow: From coding standard → rules → configuration → linting → violation repair suggestions.
• Rule Knowledge Base: Pre-built index and complete information for Linter Rules.

Why develop the tool: Mitigating AI Configuration Hallucinations

AI-assisted configuration tools, while powerful, can sometimes misinterpret coding standards and generate incorrect or incomplete configurations.

LintConfig mitigates this by:

1. Compiling coding standards into structured, standardized coding rules.
2. Checking linter documentation to ensure valid configurations.

Installation

Prerequisites

• Python 3.x (for rule data preparation scripts, if needed)
• Access to one of the supported AI coding assistants
• Install ripgrep to enable search tool in Claude Code. See this issue for details.

Recom