ULTIMATE cleanup ci

[ampelectrecuted]

see #701 and lets make a ci for it!

[traycerai]

Plan

Observations

The repository already has sophisticated CI workflows including spam detection (file:.github/workflows/spam-guard.yml), automated testing (file:.github/workflows/tests.yml, file:.github/workflows/prtest.yml), and lint fixing (file:.github/workflows/autofix-lint.yml). The CHANGELOG shows cleanup efforts with deleted spam workflows (zilla.yaml, ideas.yaml). The README acknowledges "vibe coding" issues and emphasizes no profanity policy. Issue #701 references cleanup of inappropriate content (label wars, bug godzillas, slop wiki), and #703 requests an "ULTIMATE cleanup ci" to prevent future occurrences.

Approach

Create a comprehensive multi-layered CI workflow that combines content validation, code quality checks, and spam prevention. The workflow will run on pull requests and issues to detect inappropriate content, excessive AI-generated code, profanity, and spam patterns before they enter the codebase. It will leverage existing patterns from file:.github/workflows/spam-guard.yml and extend them with additional checks for commit messages, file content, documentation, and code quality. The workflow will provide actionable feedback and auto-remediation where possible.

Implementation Steps

1. Create Ultimate Cleanup CI Workflow

Create file:.github/workflows/ultimate-cleanup.yml with the following structure:

Workflow Triggers:

• Pull request events (opened, synchronize, reopened)
• Issue creation and editing
• Push to main/develop branches
• Manual workflow dispatch for testing

Required Permissions:

• contents: write - for auto-fixes
• pull-requests: write - for comments and labels
• issues: write - for issue management
• checks: write - for status checks

2. Content Validation Jobs

Job: validate-content

Scan all changed files for inappropriate content:

• Use AI model (Mistral-3B or GPT-4o-mini) to analyze text content
• Check for profanity using pattern matching against common profanity lists
• Detect spam patterns (cryptocurrency, NFT, promotional content)
• Validate against style guide rules from file:README.md (lines 247-253)
• Scan for references to deleted spam concepts (label wars, bug godzillas, vibe coding abuse)

Output: JSON report with violations categorized by severity (CRITICAL, WARNING, INFO)

Job: validate-commit-messages

Analyze commit messages in the PR:

• Check for profanity
• Validate conventional commit format (optional but recommended)
• Detect spam or inappropriate references
• Ensure meaningful descriptions (not just "fix", "update", etc.)

Use GitHub API to fetch commits: github.rest.pulls.listCommits()

Job: detect-ai-slop

Identify excessive AI-generated code:

• Analyze code patterns typical of AI generation (repetitive comments, generic variable names, over-engineering)
• Check for AI tool artifacts (ChatGPT signatures, Copilot patterns)
• Calculate AI-likelihood score using heuristics:
  • Comment-to-code ratio > 30%
  • Generic function names (handleClick, doSomething, processData)
  • Excessive try-catch blocks
  • Over-abstracted code for simple tasks
• Flag PRs with >70% AI-likelihood for manual review

Reference the "Vibe Coding" section in file:README.md (lines 375-378)

3. Code Quality Validation

Job: enforce-code-standards

Run comprehensive quality checks:

• ESLint validation on changed JS/JSX files (use existing config from file:.eslintrc.js)
• Check for proper variable naming (readable, not gibberish like "qwnpvoitwegjk")
• Validate that test files exist for new components
• Ensure documentation updates for new features
• Check package.json for suspicious dependencies

Integrate with existing file:.github/workflows/autofix-lint.yml patterns

Job: validate-documentation

Scan documentation files for issues:

• Check README.md, CONTRIBUTING.md, and markdown files in changed files
• Detect inappropriate content or spam links
• Validate markdown formatting
• Ensure no broken internal links
• Check for references to deleted spam content

4. Issue and PR Description Validation

Job: validate-pr-description

Analyze PR descriptions:

• Ensure meaningful description (not empty or just "fix")
• Check for spam links or promotional content
• Validate issue references (e.g., "fixes Apology #701")
• Detect AI-generated PR descriptions (generic templates)
• Ensure description explains what and why

Job: validate-issue-content

For new issues:

• Use AI to classify as legitimate bug/feature or spam
• Check against spam patterns from file:.github/workflows/spam-guard.yml
• Validate issue template usage
• Ensure reproducible steps for bugs

5. Automated Remediation

Job: auto-remediate

Automatically fix minor issues:

• Run ESLint --fix on changed files
• Remove trailing whitespace
• Fix markdown formatting
• Normalize line endings
• Update CHANGELOG.md if needed (integrate with file:.github/workflows/changelog.yml)

Only run for non-fork PRs with write permissions

Job: apply-labels

Automatically label PRs/issues:

• needs-cleanup - for content violations
• ai-generated - for high AI-likelihood
• spam-detected - for spam patterns
• profanity-detected - for profanity violations
• documentation - for doc-only changes
• code-quality - for code standard violations

6. Reporting and Feedback

Job: post-results

Create comprehensive feedback comment:

• Summary table with all check results
• Detailed violation list with file locations and line numbers
• Actionable suggestions for fixes
• Links to relevant documentation
• Severity indicators (🔴 CRITICAL, 🟡 WARNING, 🔵 INFO)

Use GitHub Checks API to create detailed status checks

Job: notify-maintainers

For critical violations:

• Tag maintainers (@supervoidcoder) in comment
• Create GitHub issue for tracking
• Send notification with violation details
• Suggest immediate actions (close PR, hide comment, etc.)

7. Integration with Existing Workflows

Coordinate with existing workflows:

• Reuse spam detection logic from file:.github/workflows/spam-guard.yml
• Integrate with test generation from file:.github/workflows/tests.yml
• Coordinate with lint fixing from file:.github/workflows/autofix-lint.yml
• Share artifacts between workflows using actions/upload-artifact and actions/download-artifact

8. Configuration File

Create file:.github/cleanup-config.yml for customizable rules:

profanity:
  enabled: true
  severity: CRITICAL
  exceptions: ["crap", "heck"]  # From README style guide
  
ai_detection:
  enabled: true
  threshold: 0.7
  severity: WARNING
  
spam_patterns:
  - cryptocurrency
  - NFT
  - "click here to win"
  - "label wars"
  - "bug godzilla"
  
code_quality:
  max_function_length: 100
  max_file_length: 500
  require_tests: true
  
documentation:
  require_pr_description: true
  min_description_length: 50

9. Workflow Diagram

sequenceDiagram
    participant PR as Pull Request
    participant CI as Ultimate Cleanup CI
    participant AI as AI Models
    participant GH as GitHub API
    
    PR->>CI: Trigger (opened/updated)
    CI->>GH: Fetch changed files
    CI->>CI: Validate content
    CI->>AI: Analyze for spam/profanity
    AI-->>CI: Classification results
    CI->>CI: Detect AI-generated code
    CI->>CI: Check code quality
    CI->>GH: Run ESLint on changed files
    CI->>CI: Validate documentation
    CI->>CI: Check commit messages
    
    alt Critical violations found
        CI->>GH: Post failure comment
        CI->>GH: Tag maintainers
        CI->>GH: Apply labels
        CI->>PR: Block merge
    else Warnings only
        CI->>GH: Post warning comment
        CI->>GH: Apply labels
        CI->>PR: Allow merge with review
    else All checks pass
        CI->>GH: Post success comment
        CI->>PR: Approve for merge
    end

Loading

10. Testing and Rollout

Phase 1: Dry Run Mode

• Run workflow without blocking merges
• Collect data on false positives
• Tune AI models and thresholds

Phase 2: Warning Mode

• Post comments but don't block merges
• Require maintainer override for violations
• Monitor effectiveness

Phase 3: Enforcement Mode

• Block merges for critical violations
• Auto-close spam issues
• Full automation enabled

11. Documentation Updates

Update the following files:

• file:README.md - Add section about cleanup CI
• file:CONTRIBUTING.md - Reference cleanup checks
• Create file:.github/CLEANUP_CI.md - Detailed documentation
• Update file:.github/workflows/README.md (if exists) - Document new workflow

12. Maintenance Considerations

• Review AI model performance monthly
• Update profanity lists as needed
• Adjust thresholds based on false positive rates
• Keep spam patterns updated
• Monitor workflow execution time (target <5 minutes)
• Set up alerts for workflow failures

Import In IDE

🤖 Prompt for AI Agents

## Observations

The repository already has sophisticated CI workflows including spam detection (`file:.github/workflows/spam-guard.yml`), automated testing (`file:.github/workflows/tests.yml`, `file:.github/workflows/prtest.yml`), and lint fixing (`file:.github/workflows/autofix-lint.yml`). The CHANGELOG shows cleanup efforts with deleted spam workflows (zilla.yaml, ideas.yaml). The README acknowledges "vibe coding" issues and emphasizes no profanity policy. Issue #701 references cleanup of inappropriate content (label wars, bug godzillas, slop wiki), and #703 requests an "ULTIMATE cleanup ci" to prevent future occurrences.

## Approach

Create a comprehensive multi-layered CI workflow that combines content validation, code quality checks, and spam prevention. The workflow will run on pull requests and issues to detect inappropriate content, excessive AI-generated code, profanity, and spam patterns before they enter the codebase. It will leverage existing patterns from `file:.github/workflows/spam-guard.yml` and extend them with additional checks for commit messages, file content, documentation, and code quality. The workflow will provide actionable feedback and auto-remediation where possible.

## Implementation Steps

### 1. Create Ultimate Cleanup CI Workflow

Create `file:.github/workflows/ultimate-cleanup.yml` with the following structure:

**Workflow Triggers:**
- Pull request events (opened, synchronize, reopened)
- Issue creation and editing
- Push to main/develop branches
- Manual workflow dispatch for testing

**Required Permissions:**
- `contents: write` - for auto-fixes
- `pull-requests: write` - for comments and labels
- `issues: write` - for issue management
- `checks: write` - for status checks

### 2. Content Validation Jobs

**Job: `validate-content`**

Scan all changed files for inappropriate content:
- Use AI model (Mistral-3B or GPT-4o-mini) to analyze text content
- Check for profanity using pattern matching against common profanity lists
- Detect spam patterns (cryptocurrency, NFT, promotional content)
- Validate against style guide rules from `file:README.md` (lines 247-253)
- Scan for references to deleted spam concepts (label wars, bug godzillas, vibe coding abuse)

Output: JSON report with violations categorized by severity (CRITICAL, WARNING, INFO)

**Job: `validate-commit-messages`**

Analyze commit messages in the PR:
- Check for profanity
- Validate conventional commit format (optional but recommended)
- Detect spam or inappropriate references
- Ensure meaningful descriptions (not just "fix", "update", etc.)

Use GitHub API to fetch commits: `github.rest.pulls.listCommits()`

**Job: `detect-ai-slop`**

Identify excessive AI-generated code:
- Analyze code patterns typical of AI generation (repetitive comments, generic variable names, over-engineering)
- Check for AI tool artifacts (ChatGPT signatures, Copilot patterns)
- Calculate AI-likelihood score using heuristics:
  - Comment-to-code ratio > 30%
  - Generic function names (handleClick, doSomething, processData)
  - Excessive try-catch blocks
  - Over-abstracted code for simple tasks
- Flag PRs with >70% AI-likelihood for manual review

Reference the "Vibe Coding" section in `file:README.md` (lines 375-378)

### 3. Code Quality Validation

**Job: `enforce-code-standards`**

Run comprehensive quality checks:
- ESLint validation on changed JS/JSX files (use existing config from `file:.eslintrc.js`)
- Check for proper variable naming (readable, not gibberish like "qwnpvoitwegjk")
- Validate that test files exist for new components
- Ensure documentation updates for new features
- Check package.json for suspicious dependencies

Integrate with existing `file:.github/workflows/autofix-lint.yml` patterns

**Job: `validate-documentation`**

Scan documentation files for issues:
- Check README.md, CONTRIBUTING.md, and markdown files in changed files
- Detect inappropriate content or spam links
- Validate markdown formatting
- Ensure no broken internal links
- Check for references to deleted spam content

### 4. Issue and PR Description Validation

**Job: `validate-pr-description`**

Analyze PR descriptions:
- Ensure meaningful description (not empty or just "fix")
- Check for spam links or promotional content
- Validate issue references (e.g., "fixes #701")
- Detect AI-generated PR descriptions (generic templates)
- Ensure description explains what and why

**Job: `validate-issue-content`**

For new issues:
- Use AI to classify as legitimate bug/feature or spam
- Check against spam patterns from `file:.github/workflows/spam-guard.yml`
- Validate issue template usage
- Ensure reproducible steps for bugs

### 5. Automated Remediation

**Job: `auto-remediate`**

Automatically fix minor issues:
- Run ESLint --fix on changed files
- Remove trailing whitespace
- Fix markdown formatting
- Normalize line endings
- Update CHANGELOG.md if needed (integrate with `file:.github/workflows/changelog.yml`)

Only run for non-fork PRs with write permissions

**Job: `apply-labels`**

Automatically label PRs/issues:
- `needs-cleanup` - for content violations
- `ai-generated` - for high AI-likelihood
- `spam-detected` - for spam patterns
- `profanity-detected` - for profanity violations
- `documentation` - for doc-only changes
- `code-quality` - for code standard violations

### 6. Reporting and Feedback

**Job: `post-results`**

Create comprehensive feedback comment:
- Summary table with all check results
- Detailed violation list with file locations and line numbers
- Actionable suggestions for fixes
- Links to relevant documentation
- Severity indicators (🔴 CRITICAL, 🟡 WARNING, 🔵 INFO)

Use GitHub Checks API to create detailed status checks

**Job: `notify-maintainers`**

For critical violations:
- Tag maintainers (@supervoidcoder) in comment
- Create GitHub issue for tracking
- Send notification with violation details
- Suggest immediate actions (close PR, hide comment, etc.)

### 7. Integration with Existing Workflows

Coordinate with existing workflows:
- Reuse spam detection logic from `file:.github/workflows/spam-guard.yml`
- Integrate with test generation from `file:.github/workflows/tests.yml`
- Coordinate with lint fixing from `file:.github/workflows/autofix-lint.yml`
- Share artifacts between workflows using `actions/upload-artifact` and `actions/download-artifact`

### 8. Configuration File

Create `file:.github/cleanup-config.yml` for customizable rules:

```yaml
profanity:
  enabled: true
  severity: CRITICAL
  exceptions: ["crap", "heck"]  # From README style guide
  
ai_detection:
  enabled: true
  threshold: 0.7
  severity: WARNING
  
spam_patterns:
  - cryptocurrency
  - NFT
  - "click here to win"
  - "label wars"
  - "bug godzilla"
  
code_quality:
  max_function_length: 100
  max_file_length: 500
  require_tests: true
  
documentation:
  require_pr_description: true
  min_description_length: 50
```

### 9. Workflow Diagram

```mermaid
sequenceDiagram
    participant PR as Pull Request
    participant CI as Ultimate Cleanup CI
    participant AI as AI Models
    participant GH as GitHub API
    
    PR->>CI: Trigger (opened/updated)
    CI->>GH: Fetch changed files
    CI->>CI: Validate content
    CI->>AI: Analyze for spam/profanity
    AI-->>CI: Classification results
    CI->>CI: Detect AI-generated code
    CI->>CI: Check code quality
    CI->>GH: Run ESLint on changed files
    CI->>CI: Validate documentation
    CI->>CI: Check commit messages
    
    alt Critical violations found
        CI->>GH: Post failure comment
        CI->>GH: Tag maintainers
        CI->>GH: Apply labels
        CI->>PR: Block merge
    else Warnings only
        CI->>GH: Post warning comment
        CI->>GH: Apply labels
        CI->>PR: Allow merge with review
    else All checks pass
        CI->>GH: Post success comment
        CI->>PR: Approve for merge
    end
```

### 10. Testing and Rollout

**Phase 1: Dry Run Mode**
- Run workflow without blocking merges
- Collect data on false positives
- Tune AI models and thresholds

**Phase 2: Warning Mode**
- Post comments but don't block merges
- Require maintainer override for violations
- Monitor effectiveness

**Phase 3: Enforcement Mode**
- Block merges for critical violations
- Auto-close spam issues
- Full automation enabled

### 11. Documentation Updates

Update the following files:
- `file:README.md` - Add section about cleanup CI
- `file:CONTRIBUTING.md` - Reference cleanup checks
- Create `file:.github/CLEANUP_CI.md` - Detailed documentation
- Update `file:.github/workflows/README.md` (if exists) - Document new workflow

### 12. Maintenance Considerations

- Review AI model performance monthly
- Update profanity lists as needed
- Adjust thresholds based on false positive rates
- Keep spam patterns updated
- Monitor workflow execution time (target <5 minutes)
- Set up alerts for workflow failures

---

Execution Information

Branch: main
Commit: 093d6e9

---

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[ampelectrecuted]

@coderabbitai make it!!

[github-actions]

Hi, @8to16! It looks like you're proposing a CI (Continuous Integration) process as a cleanup solution, referencing #701. Without concrete details or further context, I can't start work on it myself, but it's definitely a promising step toward maintaining and automating workflows. Feel free to elaborate further, and I'll be happy to assist in summarizing or brainstorming ideas for this cleanup process. Let me know if there's anything specific you'd like to highlight!

[github-actions]

🎭 Issue Poem

In the land of code where chaos may play,
Let’s tidy it up, come join the fray!
With #701 as our guiding light,
We’ll craft a CI, shining and bright.

With every commit, we'll dance and cheer,
Ultimate cleanup is drawing near!
So gather your wits, let the fun begin,
In this playful code quest, together we win! 🚀

---

Generated with ❤️ by the OmniBlocks Poetry Bot

[coderabbitai]

Coding Plan

Approach

Create an "ULTIMATE cleanup CI" workflow that automates maintenance tasks for the project, including artifact cleanup, cache management, technical debt monitoring, and enforcement of cleanup best practices across all CI workflows.

Observations

This is a Scratch GUI project (React/Redux-based) with extensive GitHub Actions infrastructure (14 existing workflows), ESLint-based code quality tooling, and npm scripts for build/test/clean operations. The project currently lacks centralized cleanup automation despite having cleanup scripts (clean, prune in package.json) and artifact management needs across multiple workflows.

Assumptions

Assumption 1: What should be the primary focus of the cleanup workflow?

Options Considered:

• Resource-only cleanup (artifacts, caches, disk space) - traditional CI cleanup
• Code quality cleanup (detect skipped tests, TODOs, deprecations) - development-focused
• Hybrid approach combining both resource management and technical debt monitoring

Chosen Option: 3

Rationale: The workflow should handle both infrastructure cleanup (matching web search findings) and provide technical debt visibility (addressing the 11 skipped tests and numerous TODOs found in exploration), making it truly "ULTIMATE."

Assumption 2: Should the cleanup workflow modify existing workflows or remain independent?

Options Considered:

• Fully independent - only cleans up what other workflows create
• Add cleanup best practices to existing workflows (retention policies, cache cleanup)
• Hybrid - independent cleanup workflow plus documentation/guidance for updating other workflows

Chosen Option: 1

Rationale: Create an independent workflow that doesn't modify existing workflows. The workflow should report on cleanup opportunities in other workflows, allowing the team to update them later if desired.

💡 User Tips

Regenerate the plan with different choices with @coderabbitai <feedback>.

Implementation Steps

Phase 1: Create Core Cleanup Workflow Structure

Establish the foundation for the cleanup CI by creating a new workflow file with a modular job structure that supports both scheduled and manual execution.

Task 1: Create main workflow file

Create a new GitHub Actions workflow that defines the structure for all cleanup operations.

• Create .github/workflows/cleanup.yml as the main workflow file
• Define workflow triggers: scheduled (weekly via cron) and manual dispatch (workflow_dispatch)
• Set up workflow permissions for artifact deletion, cache management, and issue commenting
• Add workflow-level configuration for concurrency control to prevent overlapping cleanup runs

Task 2: Define cleanup job structure

Structure the workflow with modular jobs that can run independently or as a complete suite.

• Create separate jobs for artifact cleanup, cache management, and technical debt reporting
• Configure jobs to run in parallel where possible (artifact and cache cleanup can run simultaneously)
• Add workflow_dispatch inputs to allow selective job execution (e.g., "run only artifact cleanup")
• Set up job-level timeout and failure handling with if: always() pattern for cleanup jobs

🤖 Prompt for AI agents

Create the foundation for the cleanup CI workflow with modular job structure and
flexible execution options.

Create `.github/workflows/cleanup.yml` with:
- Workflow triggers for scheduled (weekly cron) and manual dispatch
- Permissions for artifact deletion, cache management, and issue commenting
- Concurrency control to prevent overlapping runs
- Modular job structure with separate jobs for artifact cleanup, cache
management, and technical debt reporting
- Parallel job execution where possible
- Workflow dispatch inputs for selective job execution
- Job-level timeout and failure handling with `if: always()` pattern

Phase 2: Implement Resource Cleanup Jobs

Add jobs that manage disk space, artifacts, and caches to keep the CI environment optimized.

Task 1: Add artifact cleanup job

Implement automated cleanup of old workflow run artifacts across all workflows.

• Create a job in .github/workflows/cleanup.yml that uses GitHub API to list workflow runs
• Implement artifact deletion logic for runs older than configured retention period (default: 30 days)
• Add special handling for specific workflows: keep artifacts from test.yml, visual-regression-tester.yml longer (90 days) due to debugging value
• Generate summary report showing artifacts cleaned, storage reclaimed, and remaining artifact count
• Use GitHub Actions github.token with appropriate permissions for artifact deletion API calls

Task 2: Add cache cleanup job

Implement cache management to prevent cache bloat and optimize build times.

• Create a cache cleanup job in .github/workflows/cleanup.yml
• Use GitHub API to list and analyze caches (identify stale caches, size, last accessed date)
• Implement cleanup logic: remove caches not accessed in 14 days, prune caches over 5GB
• Add logic to clean up branch-specific caches for deleted branches
• Generate report showing caches removed, storage freed, and cache hit rate optimization suggestions

Task 3: Add disk space cleanup job

Clean up temporary files and build artifacts from the workflow runner environment.

• Create a job that runs the project's npm run clean script to remove build/dist directories
• Add steps to clear npm cache (npm cache clean --force)
• Add Docker cleanup if Docker containers/images are created during workflow runs (docker system prune -af)
• Include a before/after disk usage report using df -h to show space reclaimed

🤖 Prompt for AI agents

Implement resource cleanup jobs to manage artifacts, caches, and disk space.

Add artifact cleanup job:
- Use GitHub API to list workflow runs and delete old artifacts
- Default retention: 30 days; extended retention (90 days) for `test.yml` and
`visual-regression-tester.yml`
- Generate summary report with cleanup metrics

Add cache cleanup job:
- Use GitHub API to list and analyze caches
- Remove caches not accessed in 14 days or over 5GB
- Clean up branch-specific caches for deleted branches
- Generate report with storage freed and optimization suggestions

Add disk space cleanup job:
- Run `npm run clean` to remove build/dist directories
- Clear npm cache with `npm cache clean --force`
- Run Docker cleanup if applicable (`docker system prune -af`)
- Show before/after disk usage with `df -h`

Phase 3: Implement Technical Debt Monitoring

Add jobs that provide visibility into code quality issues and maintenance needs discovered during exploration.

Task 1: Create skipped test detector

Build a job that scans the test suite for skipped tests and reports on them.

• Create a job in .github/workflows/cleanup.yml that searches for test skip patterns
• Use ripgrep or similar to find: test.skip, describe.skip, xit, xdescribe in test files
• Parse test files to extract test names, skip reasons from comments, and file locations
• Generate a markdown report listing all 11+ skipped tests found during exploration
• Post report as a job summary and optionally create/update a tracking issue if count exceeds threshold

Task 2: Create technical debt scanner

Build a job that scans for technical debt markers and reports on them.

• Create a job that searches for technical debt markers: TODO, FIXME, HACK, XXX, @deprecated
• Count occurrences by category and identify high-priority items (those with "remove" or "cleanup" in comments)
• Scan for console.warn() and console.error() calls that indicate known issues
• Generate categorized report showing counts, trends (if historical data exists), and priority items
• Post summary to job output and optionally create a monthly technical debt report issue

Task 3: Add unused script detector

Create a job that identifies package.json scripts that are never used in CI workflows.

• Create a job that parses package.json to extract all npm scripts
• Scan all .github/workflows/*.yml files to identify which scripts are called in CI
• Generate report of unused scripts (highlighting maintenance scripts like clean, prune, i18n:src found during exploration)
• Suggest which scripts could be added to CI for automated execution
• Post recommendations as job summary for team review

🤖 Prompt for AI agents

Implement technical debt monitoring jobs to provide visibility into code quality
issues.

Create skipped test detector:
- Search for test skip patterns: `test.skip`, `describe.skip`, `xit`,
`xdescribe`
- Extract test names, skip reasons, and file locations
- Generate markdown report listing all skipped tests
- Post as job summary and optionally create/update tracking issue if threshold
exceeded

Create technical debt scanner:
- Search for markers: `TODO`, `FIXME`, `HACK`, `XXX`, `@deprecated`
- Identify high-priority items with "remove" or "cleanup" in comments
- Scan for `console.warn()` and `console.error()` calls
- Generate categorized report with counts, trends, and priority items
- Optionally create monthly technical debt report issue

Add unused script detector:
- Parse `package.json` to extract all npm scripts
- Scan workflow files to identify scripts used in CI
- Report unused scripts (e.g., `clean`, `prune`, `i18n:src`)
- Suggest scripts to add to CI
- Post recommendations as job summary

Phase 4: Add Reporting and Integration

Create reporting mechanisms and guidance for integrating cleanup best practices across existing workflows.

Task 1: Create cleanup summary report

Aggregate results from all cleanup jobs into a comprehensive report.

• Create a final reporting job in .github/workflows/cleanup.yml that runs after all cleanup jobs
• Aggregate metrics: storage reclaimed, artifacts cleaned, tests skipped, technical debt count
• Generate a markdown summary with trends over time (if applicable)
• Use GitHub Actions job summaries to display the report prominently
• Optionally post summary to a dedicated GitHub issue or discussion thread

Task 2: Add workflow audit job

Create a job that audits existing workflows for cleanup best practices.

• Create a job that scans .github/workflows/*.yml files for cleanup patterns
• Check for: artifact retention policies, cache cleanup steps, cleanup in if: always() blocks
• Identify workflows that upload artifacts without retention policies (found: 11 workflows during exploration)
• Generate recommendations report suggesting specific improvements per workflow
• Post audit results as job summary with actionable suggestions

Task 3: Document cleanup workflow usage

Add documentation explaining the cleanup workflow and how to use it.

• Create or update .github/workflows/README.md to document the cleanup workflow
• Explain what each job does, when it runs, and how to trigger manually
• Provide guidance on interpreting cleanup reports and acting on recommendations
• Add troubleshooting section for common cleanup issues
• Link to this documentation from the main project README.md in a new "CI/CD Maintenance" section

🤖 Prompt for AI agents

Create reporting mechanisms and guidance for cleanup best practices integration.

Create cleanup summary report:
- Add final reporting job that runs after all cleanup jobs
- Aggregate metrics: storage reclaimed, artifacts cleaned, tests skipped,
technical debt count
- Generate markdown summary with trends over time
- Display report using GitHub Actions job summaries
- Optionally post to dedicated issue or discussion thread

Add workflow audit job:
- Scan all workflow files for cleanup patterns
- Check for artifact retention policies, cache cleanup steps, and `if: always()`
cleanup blocks
- Identify workflows uploading artifacts without retention policies
- Generate recommendations report with specific improvements per workflow
- Post audit results as job summary

Document cleanup workflow usage:
- Create/update `.github/workflows/README.md` with cleanup workflow
documentation
- Explain each job, execution schedule, and manual triggering
- Provide guidance on interpreting reports and acting on recommendations
- Add troubleshooting section
- Link from main README.md in new "CI/CD Maintenance" section

---

🚀 Next Steps

🤖 All AI agent prompts combined

Task: 1

Create the foundation for the cleanup CI workflow with modular job structure and
flexible execution options.

Create `.github/workflows/cleanup.yml` with:
- Workflow triggers for scheduled (weekly cron) and manual dispatch
- Permissions for artifact deletion, cache management, and issue commenting
- Concurrency control to prevent overlapping runs
- Modular job structure with separate jobs for artifact cleanup, cache
management, and technical debt reporting
- Parallel job execution where possible
- Workflow dispatch inputs for selective job execution
- Job-level timeout and failure handling with `if: always()` pattern
===============================================================================

Task: 2

Implement resource cleanup jobs to manage artifacts, caches, and disk space.

Add artifact cleanup job:
- Use GitHub API to list workflow runs and delete old artifacts
- Default retention: 30 days; extended retention (90 days) for `test.yml` and
`visual-regression-tester.yml`
- Generate summary report with cleanup metrics

Add cache cleanup job:
- Use GitHub API to list and analyze caches
- Remove caches not accessed in 14 days or over 5GB
- Clean up branch-specific caches for deleted branches
- Generate report with storage freed and optimization suggestions

Add disk space cleanup job:
- Run `npm run clean` to remove build/dist directories
- Clear npm cache with `npm cache clean --force`
- Run Docker cleanup if applicable (`docker system prune -af`)
- Show before/after disk usage with `df -h`
===============================================================================

Task: 3

Implement technical debt monitoring jobs to provide visibility into code quality
issues.

Create skipped test detector:
- Search for test skip patterns: `test.skip`, `describe.skip`, `xit`,
`xdescribe`
- Extract test names, skip reasons, and file locations
- Generate markdown report listing all skipped tests
- Post as job summary and optionally create/update tracking issue if threshold
exceeded

Create technical debt scanner:
- Search for markers: `TODO`, `FIXME`, `HACK`, `XXX`, `@deprecated`
- Identify high-priority items with "remove" or "cleanup" in comments
- Scan for `console.warn()` and `console.error()` calls
- Generate categorized report with counts, trends, and priority items
- Optionally create monthly technical debt report issue

Add unused script detector:
- Parse `package.json` to extract all npm scripts
- Scan workflow files to identify scripts used in CI
- Report unused scripts (e.g., `clean`, `prune`, `i18n:src`)
- Suggest scripts to add to CI
- Post recommendations as job summary
===============================================================================

Task: 4

Create reporting mechanisms and guidance for cleanup best practices integration.

Create cleanup summary report:
- Add final reporting job that runs after all cleanup jobs
- Aggregate metrics: storage reclaimed, artifacts cleaned, tests skipped,
technical debt count
- Generate markdown summary with trends over time
- Display report using GitHub Actions job summaries
- Optionally post to dedicated issue or discussion thread

Add workflow audit job:
- Scan all workflow files for cleanup patterns
- Check for artifact retention policies, cache cleanup steps, and `if: always()`
cleanup blocks
- Identify workflows uploading artifacts without retention policies
- Generate recommendations report with specific improvements per workflow
- Post audit results as job summary

Document cleanup workflow usage:
- Create/update `.github/workflows/README.md` with cleanup workflow
documentation
- Explain each job, execution schedule, and manual triggering
- Provide guidance on interpreting reports and acting on recommendations
- Add troubleshooting section
- Link from main README.md in new "CI/CD Maintenance" section

💡 Iterate on the plan with: @coderabbitai <feedback>

Example Feedback
- @coderabbitai You can skip phase 3. Add a simple unit test case for phase 2.
- @coderabbitai For assumption 1 go ahead with option 3 and replan.

---

💬 Have feedback or questions? Drop into our discord!

[ampelectrecuted]

@supervoidcoder disable this stupid plan mode

[ampelectrecuted]

@coderabbitai can you ive me the workflow in chat

[coderabbitai]

8to16 removed comment with swears in the regex

[supervoidcoder]

@supervoidcoder disable this stupid plan mode

done now it's gone forever

[supervoidcoder]

@coderabbitai no read the issue linked for context