⚡ EXECUTION INSTRUCTIONS FOR CLAUDE

When this command is invoked, YOU (Claude) must execute these steps immediately: This is NOT documentation - these are COMMANDS to execute right now. Use TodoWrite to track progress through multi-phase workflows.

🚨 EXECUTION WORKFLOW

Phase 1: Execute Documented Workflow

Action Steps:

1. Review the reference documentation below and execute the detailed steps sequentially.

📋 REFERENCE DOCUMENTATION

Claude Commands Directory

🎯 Philosophy: Explicit > Implicit

This directory contains Claude Code slash commands that follow the explicit execution principle:

• ✅ Documentation-driven workflows where every command is visible
• ✅ Python-based tools for maintainability and cross-platform support
• ✅ No hidden wrapper scripts - users see exactly what runs
• ❌ Shell scripts are NOT preferred for command implementations

📋 Command Guidelines

✅ Preferred Approach: Python + Documentation

• Python scripts (.py) for command logic and data processing
• Markdown documentation (.md) with explicit command sequences
• Direct tool calls that users can see and customize

Example workflow in copilot.md:

# Explicit execution - user sees every command

python3 .claude/commands/copilot.py 780
./run_ci_replica.sh
gh pr view 780 --json statusCheckRollup
python3 .claude/commands/copilot_resolver.py [files]

❌ Avoid: Shell Script Wrappers

• No .sh wrappers that hide the actual commands being executed
• No abstraction layers that obscure the workflow
• No "magic" scripts that users can't easily understand or modify

🚨 NEVER Create copilot.sh Again

This was tried and failed because it:

• Hid commands behind a wrapper interface
• Contradicted the explicit execution philosophy
• Added unnecessary complexity vs. documentation-driven approach
• Made the workflow less transparent and harder to customize

🏗️ Command Architecture Patterns

Data Collection Commands

• Pattern: command.py with clear argument parsing
• Output: Structured data files in /tmp/ for LLM analysis
• Integration: Via explicit calls in documentation

Analysis Commands

• Pattern: Standalone Python scripts that process collected data
• Focus: Single responsibility (conflict resolution, formatting, etc.)
• Usage: Direct execution with clear input/output contracts

Workflow Documentation

• Pattern: command.md with step-by-step explicit execution
• Includes: Decision trees, troubleshooting, examples
• Goal: User can execute any step manually or automate selectively

📁 Current Commands

copilot.py - Enhanced PR Analysis

• Purpose: Data collection, conflict detection, auto-fixing
• Usage: python3 copilot.py [PR] [flags]
• Flags: --auto-fix, --merge-conflicts, --threaded-reply

copilot_resolver.py - Conflict Resolution

• Purpose: Automated conflict resolution with safety mechanisms
• Usage: python3 copilot_resolver.py file1.py file2.py
• Features: Backup, validation, rollback

savetmp.py - Evidence Archiver

• Purpose: Creates timestamped /tmp/<repo>/<branch>/<work>/ evidence packages with methodology, notes, and copied artifacts.
• Usage: python .claude/commands/savetmp.py <work_name> [--methodology ... --artifact ...]
• Outputs: README.md, section markdown files, metadata.json, and an artifacts/ directory for preserved logs/screenshots.

🔧 Development Guidelines

Adding New Commands

1. Create Python script with clear argument parsing
2. Write documentation with explicit usage examples
3. Test explicit execution - no hidden dependencies
4. Update this README with the new command

Modifying Existing Commands

1. Maintain explicit interfaces - no hidden behavior changes
2. Update documentation to reflect new capabilities
3. Test both automated and manual execution paths
4. Preserve backward compatibility where possible

Command Design Principles

• Single Responsibility: Each command does one thing well
• Clear Interfaces: Obvious inputs, outputs, and side effects
• Error Handling: Graceful degradation with helpful error messages
• Documentation: Every command has usage examples and troubleshooting

📚 Learning from copilot.sh Mistake

What Happened: Created copilot.sh as a wrapper script for CI integration Why It Failed: Contradicted explicit execution philosophy, hid commands Lesson Learned: Documentation-driven > Script-driven for transparency Never Again: No shell script wrappers for Claude commands

Correct Approach: Enhance copilot.py with CI features and document explicit usage in copilot.md

🚨 CRITICAL ANTI-PATTERNS TO AVOID

NEVER Recreate Hook Files

❌ DO NOT create copilot_pre_hook.py or copilot_post_hook.py ❌ DO NOT add copilot hook configuration to .claude/settings.toml

Modular Copilot Commands

The copilot system follows a clean architecture where only /commentfetch uses Python for data collection, and all other commands work directly through .md files:

Clean Architecture Commands

The 4 Modular Commands (Each Stands Alone):

1. /commentfetch - Fetch all PR comments ✅

   • Pure Python: Collects comments from all sources
   • Output: Returns fresh data directly (no caching)
   • The ONLY command that needs Python (pure data collection)

2. /fixpr - Analyze CI failures and conflicts ✅

   • Pure Markdown: fixpr.md provides all functionality
   • Claude reads fixpr.md and executes analysis directly
   • Runs CI checks, analyzes failures, suggests fixes
   • NO Python file needed

3. /commentreply - Post comment responses ✅

   • Pure Markdown: commentreply.md guides Claude
   • Claude generates and posts replies directly via gh api
   • NO Python middleman, NO intermediate files
   • Direct execution with full transparency

4. /pushl - Push changes to remote ✅

   • Existing command for git operations
   • Handles add, commit, push workflow

Orchestration:

• /copilot - Intelligent orchestrator that chains the above commands
• Adapts workflow based on PR needs
• Ensures 100% comment coverage with DONE/NOT DONE

Clean Architecture Principles

Minimal Python, Maximum Claude Intelligence

The clean approach:

• Python: ONLY for /commentfetch data collection
• .md files: Complete command implementations for /fixpr and /commentreply
• Claude: Executes .md instructions directly, no Python middleman
• Transparency: All actions shown before execution
• Modularity: Each command stands alone and can be chained

Clean data flow:

PHASE 1: DATA COLLECTION (Only /commentfetch uses Python)
commentfetch.py → fresh comment data
fixpr.py → fixes.json, comparison.json, conflicts.json
        ↓
PHASE 2: INTELLIGENT ANALYSIS (Claude + .md)
Claude reads data + applies .md intelligence
        ↓
PHASE 3: EXECUTION
Claude executes fixes directly
commentreply.py posts pre-generated replies
pushl handles git operations

Why Hybrid?

• Modularity: Each command can stand alone
• Composability: Commands chain together naturally
• Transparency: Clear separation of data vs intelligence
• Maintainability: Python for stable plumbing, .md for evolving intelligence

📚 Version History

v1.1.0 (2025-08-09)

Export Statistics:

• Commands: 116 command definitions
• Hooks: 13 Claude Code automation hooks
• Scripts: 5 infrastructure scripts

Changes:

• Comprehensive command system (116 commands) with enhanced automation
• Enhanced hook automation (13 hooks) including speculation detection and root file management
• Infrastructure automation (5 scripts) for development environment setup
• Template-based README generation with dynamic content replacement
• Obsolete file cleanup and maintenance for repository hygiene
• Additive export strategy preserving existing content while adding new features
• Enhanced content filtering and path normalization for cross-project compatibility
• Version tracking and change history management for transparent updates
• Single version history placement at bottom of README with table of contents integration

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Remember: If users can't see exactly what's running, it's probably the wrong approach.