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").

• 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.

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.

• 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.

Add hooks for claude code. ( Although Skills are designed to auto-activate in Claude Code, but in practice, activation is unreliable without explicit hooks. )

pnpx claude-skills-cli add-hook

Install directly in Claude Code with two commands:

/plugin marketplace add idiomaticrefactoring/LintConfig
/plugin install LintConfig@LintConfig

1. Clone or download this repository
2. Copy the skill to the appropriate skills directory for your platform:

Example:

# For Claude Code
cp -r LintConfig/skills/LintConfig ~/.claude/skills/

# For Cursor
cp -r LintConfig/skills/LintConfig ~/.cursor/skills/

Supported: Claude Code, Cursor, Windsurf, OpenCode, Gemini

The skill activates automatically when you request linter configuration or code quality tasks. Just chat naturally:

Generate Checkstyle configuration for "Package declaration
The package declaration is not line-wrapped. The column limit (Section 4.4, Column limit: 100) does not apply to package declarations."

Configure Checkstyle for Google Java Style Guide

Check this code at LintConfig/code/ArrayCombination.java against our coding standard at LintConfig/test/test_cs/package_declaration.txt and suggest fixes

Supported: GitHub Copilot, Codex

Use the slash command to invoke the skill:

/lintconfig Generate a linter configuration that enforces:
Package declaration
The package declaration is not line-wrapped. The column limit (Section 4.4, Column limit: 100) does not apply to package declarations.

/lintconfig Configure Checkstyle for Google Java Style Guide

Generate linter configuration files without executing lint checks:

Generate a Checkstyle configuration for the following coding standard:
[Your coding standard here]

Full workflow from standard → configuration → linting → repair:

Parse this coding standard, configure Checkstyle, lint my code, and suggest fixes:
[Your coding standard or file path or url]
[Your lintername]
[Your code or file path]

The skill follows a structured 5-step process:

• Identifies coding standard (from text, file, or URL)
• Determines target linter (Checkstyle, ESLint, Ruff, etc.)
• Extracts programming language and code context
• Prepares linter rule knowledge base if needed

• Parses natural language coding standards into atomic coding rules
• Uses structured grammar to represent rules formally
• Handles edge cases and clarifications as separate rules
• Outputs a complete rule set for configuration mapping

• Maps each coding rule to candidate linter rules
• Retrieves complete rule documentation and options
• Binds option values within valid ranges
• Classifies coverage relationship (Exact Match, Over/Under-Approximation)
• Filters invalid configurations
• Generates validated configuration file

• Runs the generated linter configuration against your code
• Captures and saves linting results
• Reports violations with context

• Analyzes linting violations
• Provides fix suggestions aligned with coding rules
• Helps improve code quality systematically

LintConfig/
├── data/                          # Linter rule knowledge base
│   └── Checkstyle/
│       ├── CheckstyleIndex.json   # Rule index (184 rules)
│       ├── CheckstyleConfig.xml   # Example configuration
│       └── rules/                 # Complete rule documentation
│           └── [184 JSON files]   # Individual rule details
├── prompt/                        # Prompt templates
│   ├── Prompt_Parse_CodingStandard.md
│   ├── Prompt_Configure_Linter.md
│   ├── Prompt_Prepare_LinterRuleIndex.md
│   └── Prompt_Prepare_LinterRuleCompleteInformation.md
├── output/                        # Generated configurations
│   ├── CheckstyleConfig.xml
│   ├── CONFIGURATION_SUMMARY.txt
│   └── RULE_MAPPING_TABLE.txt
├── test/                          # Test cases
│   ├── test_cs/                   # Checkstyle test files
│   └── test_task_prompt/          # Prompt examples
├── SKILL.md                       # Skill definition
└── README.md                      # This file

CHECKSTYLE CONFIGURATION SUMMARY
================================================================================

PROJECT: Google Java Style Guide - Checkstyle Configuration
Total Coding Rules Analyzed: 47
Checkstyle Rules Configured: 23

COVERAGE CLASSIFICATION
- Exact Match:           32 rules (68%)
- Over-Approximation:    5 rules (11%)
- Under-Approximation:   3 rules (6%)
- Not Covered:           7 rules (15%)

Rules Successfully Mapped: 40 out of 47 (85%)

The skill generates validated XML/JSON/YAML configuration files ready to use:

<?xml version="1.0" encoding="UTF-8"?>
<module name="Checker">
  <module name="LineLength">
    <property name="max" value="100"/>
    <property name="ignorePattern" value="^(package|import) .*"/>
  </module>
  <module name="TreeWalker">
    <module name="NoLineWrap">
      <property name="tokens" value="PACKAGE_DEF"/>
    </module>
  </module>
</module>

The skill classifies how well each linter configuration aligns with your coding rules:

• Exact Match: The linter configuration checks exactly the same violations as the coding rule
• Over-Approximation: The linter flags more issues than the coding rule requires
• Under-Approximation: The linter flags fewer issues than the coding rule requires
• Mismatches: Non-aligned cases (filtered out)

This helps you understand the limitations and make informed decisions about your linting setup.

We welcome contributions! Areas where help is needed:

1. Additional Linter Support: Add support for ESLint, Ruff, Pylint, RuboCop, etc.
2. Rule Knowledge Base: Expand rule documentation for existing and new linters
3. Language Support: Add support for more programming languages
4. Testing: Add test cases for various coding standards and edge cases
5. Documentation: Improve prompts and examples

1. Clone the repository
2. Review the existing structure in data/Checkstyle/ as a template
3. For new linters:
   • Create data/<LinterName>/ directory
   • Generate <LinterName>Index.json with rule index
   • Create rules/ directory with each rule complete documentation
4. Test with various coding standards and code samples

• Multi-platform deployment (Claude Code, Cursor, Windsurf, GitHub Copilot, OpenCode, Codex, Gemini)
• Support for 5+ major programming languages
• Support for 3-5 popular linters per language
• Enhanced prompt compatibility testing
• Automated rule knowledge base generation
• Integration with CI/CD pipelines
• CLI installer for easy multi-platform setup

Inspired by the need for automated linting configuration in AI-assisted coding workflows. Built to bridge the gap between natural language coding standards and formal linter configurations.