Add missing repository components for complete supercharge.sh installation
Added all directories and files expected by supercharge.sh: - hooks/ (5 hook scripts for session management and AI consultation) - commands/ (3 custom slash commands: brainstorm, write-plan, execute-plan) - plugins/ (plugin references for glm-plan, rust-analyzer, marketplaces) - bin/ralphloop (Ralph Orchestrator wrapper for autonomous iteration) - scripts/sync-agents.sh (agent synchronization script) - templates/ (config templates: settings, hooks, config.json) This completes the repository structure so supercharge.sh can install all components without warnings. Co-Authored-By: Claude <noreply@anthropic.com>
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uroma
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bin/ralphloop
Executable file
282
bin/ralphloop
Executable file
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#!/usr/bin/env python3
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"""
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RalphLoop - Ralph Orchestrator Wrapper for Claude Code
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This wrapper integrates Ralph Orchestrator with Claude Code skills,
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providing autonomous "Tackle Until Solved" capabilities.
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Environment Variables:
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RALPH_AGENT Agent to use (claude|gemini|kiro|q|auto)
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RALPH_MAX_ITERATIONS Maximum iterations (default: 100)
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RALPH_MAX_RUNTIME Maximum runtime in seconds (default: 14400)
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RALPH_VERBOSE Enable verbose output (default: false)
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Usage:
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ralphloop "Design a microservices architecture"
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ralphloop --agent claude --max-iterations 50 "Implement auth"
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"""
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import os
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import sys
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import json
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import argparse
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import subprocess
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from pathlib import Path
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from datetime import datetime, timedelta
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# Configuration
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DEFAULT_AGENT = os.getenv("RALPH_AGENT", "claude")
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DEFAULT_MAX_ITERATIONS = int(os.getenv("RALPH_MAX_ITERATIONS", 100))
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DEFAULT_MAX_RUNTIME = int(os.getenv("RALPH_MAX_RUNTIME", 14400))
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VERBOSE = os.getenv("RALPH_VERBOSE", "false").lower() == "true"
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# Ralph directory
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RALPH_DIR = Path(".ralph")
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STATE_FILE = RALPH_DIR / "state.json"
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PROMPT_FILE = RALPH_DIR / "PROMPT.md"
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CONFIG_FILE = RALPH_DIR / "ralph.yml"
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ITERATIONS_DIR = RALPH_DIR / "iterations"
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def setup_ralph_directory():
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"""Create .ralph directory structure."""
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RALPH_DIR.mkdir(exist_ok=True)
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ITERATIONS_DIR.mkdir(exist_ok=True)
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def load_state():
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"""Load current Ralph state."""
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if STATE_FILE.exists():
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with open(STATE_FILE, "r") as f:
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return json.load(f)
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return {
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"iteration": 0,
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"status": "not_started",
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"started_at": None,
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"completed_at": None,
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"last_error": None
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}
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def save_state(state):
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"""Save Ralph state."""
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with open(STATE_FILE, "w") as f:
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json.dump(state, f, indent=2)
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def create_prompt(task, agent, max_iterations, max_runtime):
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"""Create PROMPT.md with task and success criteria."""
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prompt = f"""# RalphLoop Task
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## Task
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{task}
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## Success Criteria
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- Task fully analyzed and understood
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- All requirements addressed
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- Implementation/design complete
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- Quality standards met
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- No critical issues remaining
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## Configuration
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- Agent: {agent}
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- Max Iterations: {max_iterations}
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- Max Runtime: {max_runtime} seconds ({timedelta(seconds=max_runtime)})
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- Started: {datetime.now().isoformat()}
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## Instructions
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Run autonomous iterations until all success criteria are met.
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Update state.json after each iteration.
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Save final result to iterations/final.md when complete.
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"""
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with open(PROMPT_FILE, "w") as f:
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f.write(prompt)
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def create_config(agent, max_iterations, max_runtime):
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"""Create ralph.yml configuration."""
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config = f"""# RalphLoop Configuration
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agent: {agent}
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max_iterations: {max_iterations}
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max_runtime: {max_runtime}
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verbose: {VERBOSE}
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# Output
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iterations_dir: iterations
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state_file: state.json
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final_output: iterations/final.md
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# Claude Code Integration
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skill_path: ~/.claude/skills/ralph
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brainstorming_skill: ~/.claude/skills/brainstorming
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"""
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with open(CONFIG_FILE, "w") as f:
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f.write(config)
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def run_ralph_iteration(task, iteration, agent):
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"""Run a single Ralph iteration."""
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iteration_file = ITERATIONS_DIR / f"{iteration:03d}.md"
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# Check if ralph-orchestrator is installed
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try:
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result = subprocess.run(
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["ralph", "--agent", agent, "--prompt", task],
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capture_output=True,
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text=True,
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timeout=300
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)
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output = result.stdout or result.stderr
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with open(iteration_file, "w") as f:
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f.write(f"""# Iteration {iteration}
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## Agent: {agent}
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## Time: {datetime.now().isoformat()}
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## Output
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{output}
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## Status
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{'COMPLETE' if result.returncode == 0 else 'IN_PROGRESS'}
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""")
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return result.returncode == 0
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except FileNotFoundError:
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# Ralph not installed, use placeholder
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with open(iteration_file, "w") as f:
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f.write(f"""# Iteration {iteration}
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## Agent: {agent}
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## Time: {datetime.now().isoformat()}
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## Note
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Ralph Orchestrator not installed. Install with:
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pip install ralph-orchestrator
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## Task Analysis
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{task}
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## Next Steps
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1. Install Ralph Orchestrator
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2. Re-run ralphloop command
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""")
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return False
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except subprocess.TimeoutExpired:
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with open(iteration_file, "w") as f:
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f.write(f"""# Iteration {iteration}
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## Agent: {agent}
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## Time: {datetime.now().isoformat()}
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## Status: TIMEOUT
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Iteration exceeded 300 second timeout.
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""")
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return False
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def run_ralph(task, agent, max_iterations, max_runtime):
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"""Run Ralph autonomous loop."""
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setup_ralph_directory()
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state = load_state()
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if state["status"] == "completed":
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print(f"Task already completed at {state['completed_at']}")
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print(f"See {ITERATIONS_DIR / 'final.md'} for results")
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return
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# Initialize
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state["status"] = "running"
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state["started_at"] = datetime.now().isoformat()
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save_state(state)
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create_prompt(task, agent, max_iterations, max_runtime)
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create_config(agent, max_iterations, max_runtime)
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start_time = datetime.now()
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completed = False
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for iteration in range(1, max_iterations + 1):
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# Check runtime
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elapsed = (datetime.now() - start_time).total_seconds()
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if elapsed > max_runtime:
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print(f"Max runtime ({max_runtime}s) exceeded")
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break
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if VERBOSE:
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print(f"[Ralph] Iteration {iteration}/{max_iterations}")
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state["iteration"] = iteration
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save_state(state)
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# Run iteration
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if run_ralph_iteration(task, iteration, agent):
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completed = True
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break
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# Finalize
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state["status"] = "completed" if completed else "max_iterations_reached"
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state["completed_at"] = datetime.now().isoformat()
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save_state(state)
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# Create final output
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final_file = ITERATIONS_DIR / "final.md"
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with open(final_file, "w") as f:
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f.write(f"""# RalphLoop Final Result
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## Task
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{task}
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## Agent: {agent}
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## Iterations: {state['iteration']}
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## Status: {state['status']}
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## Started: {state['started_at']}
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## Completed: {state['completed_at']}
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## Result
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{'Task completed successfully!' if completed else 'Max iterations reached. Manual review needed.'}
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## Output Files
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- iterations/001.md through iterations/{iteration:03d}.md
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- state.json - Progress tracking
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- ralph.yml - Configuration
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## Next Steps
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{'Implement the solution from the final iteration.' if completed else 'Review iterations and continue manually or increase max_iterations.'}
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""")
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print(f"\n[Ralph] {state['status'].upper()}")
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print(f"[Ralph] Iterations: {state['iteration']}/{max_iterations}")
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print(f"[Ralph] Output: {final_file}")
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def main():
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parser = argparse.ArgumentParser(
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description="RalphLoop - Ralph Orchestrator Wrapper",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog=__doc__
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)
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parser.add_argument("task", help="Task description")
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parser.add_argument("--agent", default=DEFAULT_AGENT, help="Agent to use")
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parser.add_argument("--max-iterations", type=int, default=DEFAULT_MAX_ITERATIONS)
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parser.add_argument("--max-runtime", type=int, default=DEFAULT_MAX_RUNTIME)
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parser.add_argument("--verbose", action="store_true")
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args = parser.parse_args()
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if args.verbose:
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global VERBOSE
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VERBOSE = True
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run_ralph(
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task=args.task,
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agent=args.agent,
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max_iterations=args.max_iterations,
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max_runtime=args.max_runtime
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)
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if __name__ == "__main__":
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main()
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