Implement automated PR linting with Claude Code/Amazon Q CLI with cost tracking

## Overview

Implement an automated workflow that uses Claude Code or Amazon Q CLI to enforce coding standards on every PR, automatically commit fixes back to the PR, and track usage costs via GitHub metrics.

## Motivation

- Ensure consistent code quality across all contributions
- Reduce manual code review burden for style/formatting issues
- Automate the correction of common linting issues
- Track and optimize AI assistant usage costs
- Provide visibility into AI tool usage patterns

## Implementation Approach

### 1. GitHub Action Setup

- Create a GitHub Action workflow that triggers on PR creation/update
- Configure the action to check out the PR code
- Set up authentication for Claude Code or Amazon Q CLI
- Run the selected AI tool against changed files

### 2. Linting Configuration

- Define coding standards in a configuration file (e.g., `.lintconfig.json`)
- Specify which rules are auto-fixable vs. requiring manual intervention
- Include language-specific standards for different file types
- Allow repository-specific overrides

### 3. Automated Fix Workflow

- Run the AI tool in fix mode on detected issues
- Generate a commit with the fixes
- Push changes back to the PR branch
- Add a comment summarizing the changes made

### 4. Cost and Usage Tracking

- Implement logging for each AI tool invocation
- Track metrics such as:
- Number of files processed
- Number of issues found/fixed
- Token usage per PR
- Estimated cost per PR
- Store metrics in a structured format for analysis

### 5. GitHub Metrics Dashboard

- Create a GitHub metrics dashboard to visualize:
- Cost trends over time
- Most common linting issues
- Cost per repository/team
- ROI in terms of developer time saved

## Technical Requirements

- GitHub Actions workflow configuration
- Authentication mechanism for AI tools (secure token storage)
- Configuration schema for linting rules
- Metrics collection and storage solution
- Dashboard for visualizing metrics

## Tool Selection Considerations

### Claude Code
- Pros: Strong natural language understanding, good at explaining issues
- Cons: May have higher latency, potentially higher cost

### Amazon Q CLI
- Pros: AWS integration, potentially lower cost for AWS customers
- Cons: May have more limited language support

## Next Steps

1. Evaluate and select the AI tool (Claude Code vs Amazon Q CLI)
2. Create proof-of-concept GitHub Action for a single repository
3. Define initial set of coding standards to enforce
4. Implement basic metrics collection
5. Test on a small set of PRs and gather feedback
6. Refine and expand to more repositories
7. Develop metrics dashboard

## Expected Benefits

- **Consistent Code Quality**: Enforce standards automatically across all repositories
- **Developer Time Savings**: Reduce time spent on manual formatting and style fixes
- **Cost Optimization**: Track and optimize AI tool usage
- **Continuous Improvement**: Identify common issues to improve documentation and training

## Alignment with Dotfiles Philosophy

This implementation aligns with our "automation mindset" principle by:
- Automating repetitive code quality tasks
- Creating reproducible linting processes across repositories
- Avoiding manual, one-off commands for code formatting
- Documenting coding standards as code

## Resources Required

- GitHub Actions minutes
- Claude Code or Amazon Q CLI usage costs
- Development time for workflow setup
- Ongoing maintenance for linting rules

No existing workflow, tests, or entry points are named. Start by evaluating Claude Code versus Amazon Q CLI, then scope a proof of concept around a GitHub Actions workflow, `.lintconfig.json`, secure authentication, metrics storage, and a dashboard; done means automated PR fixes, comments, and cost metrics work on selected repositories.

Written by the indexing model from the issue text.