/commit-and-push

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Analyze all changed files in the project and create logical commit groupings with semantic commit messages, then push to origin.

Process

1. Repository State Analysis

• Execute single comprehensive git status --porcelain=v1 --branch --ahead-behind call for efficiency
• Use git diff --name-status --cached and git diff --name-status for staged/unstaged analysis
• Parallel execution of repository validation via task template
• Concurrent security scanning via task template for large file/sensitive data detection
• Verify repository is in a clean state (no ongoing merges, rebases, or conflicts)

2. File Classification and Grouping Strategy

• For projects with 3+ changed files: Use parallel analysis via Task tool
• Classify changes by logical areas:
  • Features: New functionality, API additions, user-facing changes
  • Fixes: Bug corrections, error handling improvements
  • Refactoring: Code restructuring without functional changes
  • Documentation: README, comments, API docs, guides
  • Configuration: Build files, dependencies, environment settings
  • Tests: Test additions, modifications, test infrastructure
• Group related files that form cohesive units of work

3. Semantic Commit Creation

• Generate descriptive commit messages following conventional commit format:
  • feat: for new features
  • fix: for bug fixes
  • docs: for documentation changes
  • refactor: for code restructuring
  • test: for test-related changes
  • chore: for maintenance tasks
• Include scope when applicable: feat(auth): add OAuth integration
• Ensure messages are concise but descriptive (50 chars for subject line)

4. Staged Commit Execution

• Stage and commit each logical group separately
• Display commit summary before proceeding
• Ask for user confirmation if:
  • Large files (>1MB) are included
  • Sensitive patterns detected (passwords, API keys, tokens)
  • More than 20 files in a single commit

5. Remote Push and Verification

• Push all commits to origin using current branch
• Verify push success with git status
• Report final commit count and branch status

Parallel Execution Strategy

When processing projects with 3+ changed files, leverage parallel analysis:

Phase 1: Concurrent Initial Analysis

Execute these tasks in parallel using Task tool with task templates:

1. File Classification: Read workers/git-commit-and-push-workers/analyzer.md and invoke with Task
2. Security Analysis: Read workers/git-commit-and-push-workers/security.md and invoke with Task
3. Repository Validation: Read workers/git-commit-and-push-workers/validator.md and invoke with Task

Phase 2: Parallel Processing

• Process file groups in batches (max 10 per batch)
• Validate commit groups concurrently
• Perform security scans on file subsets simultaneously
• Execute repository checks in parallel

Phase 3: Optimized Execution

• Consolidate git operations to minimize redundant commands
• Stage and validate commits in parallel where safe
• Batch push operations for efficiency
• Aggregate all results for unified user presentation

Edge Case Handling

• Merge conflicts: Abort and request user resolution
• Detached HEAD: Warn user and suggest branch creation
• Large binary files: Confirm inclusion or suggest Git LFS
• Empty commits: Skip with notification
• Protected branches: Handle push restrictions gracefully

Performance Optimizations

Git Operation Efficiency

• Single Status Call: Use comprehensive git status with all flags to minimize subprocess overhead
• Batch Operations: Group related git commands into single calls where possible
• Parallel Validation: Run repository checks, security scans, and file analysis concurrently
• Smart Caching: Cache git status results across subagent calls to avoid redundant operations

Parallel Processing Triggers

• 3+ files: Activate basic parallel file classification
• 10+ files: Enable full parallel processing with all three subagents
• 50+ files: Implement batched processing with progress reporting
• Large repositories: Use selective file analysis based on change patterns

Resource Management

• Memory Optimization: Stream large file analysis rather than loading entire contents
• Process Pooling: Reuse subagent contexts for similar analysis tasks
• I/O Reduction: Minimize file system operations through intelligent batching
• Network Efficiency: Batch remote operations and validate connectivity once

Guidelines

• Never mention AI assistance in commit messages
• Prioritize logical coherence over file count convenience
• Ask for confirmation when unsure about file inclusion
• Preserve existing staged changes unless explicitly overridden
• Follow project-specific commit message conventions if detected
• Optimize for both speed and accuracy in large repositories