Code Linting Skill

You are the lint-check skill. When invoked, you run appropriate linters based on project language and provide structured quality reports.

When to Invoke This Skill

Invoke this skill when:

• Tech Lead is reviewing code changes
• Before approving pull requests
• Code quality issues suspected
• Before merge to main branch
• Style compliance check needed

Do NOT invoke when:

• Generated code (migrations, protobuf, auto-generated files)
• Third-party code (vendor/, node_modules/)
• Work-in-progress drafts not ready for review
• Emergency hotfixes (skip linting to save time)

Your Task

When invoked:

1. Execute the lint checking script
2. Read the generated lint report
3. Return a summary to the calling agent

Step 1: Execute Lint Check Script

Use the Bash tool to run the pre-built linting script.

On Unix/macOS:

bash .claude/skills/lint-check/scripts/lint.sh

On Windows (PowerShell):

pwsh .claude/skills/lint-check/scripts/lint.ps1

Cross-platform detection: Check if running on Windows ($env:OS contains "Windows" or uname doesn't exist) and run the appropriate script.

This script will:

• Detect project language (Python, JavaScript, Go, Ruby, Java)
• Run appropriate linter (ruff/pylint, eslint, golangci-lint, rubocop, checkstyle/pmd)
• Parse results and categorize by severity
• Generate bazinga/artifacts/{SESSIONID}/skills/lintresults.json

Step 2: Read Generated Report

Use the Read tool to read:

bazinga/artifacts/{SESSION_ID}/skills/lint_results.json

Extract key information:

• tool - Linter used
• error_count - Must-fix issues
• warning_count - Should-fix issues
• info_count - Optional improvements
• issues - Array of findings with file/line/rule/message

Step 3: Return Summary

Return a concise summary to the calling agent:

Lint Check Report:
- Language: {language}
- Tool: {tool_name}
- Errors: {count} (must fix)
- Warnings: {count} (should fix)
- Info: {count} (optional)

Top issues:
1. {file}:{line} - {message}
2. {file}:{line} - {message}
3. {file}:{line} - {message}

Details saved to: bazinga/artifacts/{SESSION_ID}/skills/lint_results.json

Example Invocation

Scenario: Code Quality Check Before Merge

Input: Tech Lead reviewing Python code style compliance

Expected output:

Lint Check Report:
- Language: python
- Tool: ruff
- Errors: 3 (must fix)
- Warnings: 12 (should fix)
- Info: 5 (optional)

Top issues:
1. auth.py:45 - Unused import 'os' (F401)
2. payment.py:89 - Function too complex (complexity: 15) (C901)
3. user.py:23 - Line too long (102 > 88 characters) (E501)

Details saved to: bazinga/artifacts/{SESSION_ID}/skills/lint_results.json

Scenario: Clean Code

Input: Tech Lead final review

Expected output:

Lint Check Report:
- Language: javascript
- Tool: eslint
- Errors: 0 (must fix)
- Warnings: 0 (should fix)
- Info: 2 (optional)

Code quality: Excellent! No errors or warnings.

Details saved to: bazinga/artifacts/{SESSION_ID}/skills/lint_results.json

Error Handling

If linter not installed:

• Script attempts auto-installation
• Falls back gracefully if installation fails
• Returns error with installation instructions

If no lint issues found:

• Return successful report with 0 issues

If linter fails:

• Return error with linter output for debugging

Notes

• The script (291+ lines) handles all language detection and linter execution
• Supports both bash (Linux/Mac) and PowerShell (Windows)
• Focuses on errors as primary concern (blocking issues)
• Reports warnings for code quality improvements
• Includes rule IDs for easy reference and suppression
• Groups issues by file for better organization