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Add infographics skill for creating visual data representations
New skill for generating scientific infographics including: - SKILL.md with comprehensive guidelines for infographic creation - Design principles and color palette references - Scripts for AI-powered infographic generation - Support for various infographic types (statistical, process, comparison, etc.) Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Vinayak Agarwal
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scientific-skills/infographics/scripts/generate_infographic.py
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scientific-skills/infographics/scripts/generate_infographic.py
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#!/usr/bin/env python3
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"""
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Generate professional infographics using Nano Banana Pro.
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This script generates infographics with smart iterative refinement:
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- Uses Nano Banana Pro (Gemini 3 Pro Image Preview) for generation
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- Uses Gemini 3 Pro for quality review
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- Only regenerates if quality is below threshold
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- Supports 10 infographic types and industry style presets
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Usage:
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python generate_infographic.py "5 benefits of exercise" -o benefits.png --type list
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python generate_infographic.py "Company history 2010-2025" -o timeline.png --type timeline --style corporate
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python generate_infographic.py --list-options
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"""
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import argparse
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import os
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import subprocess
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import sys
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from pathlib import Path
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# Available options for quick reference
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INFOGRAPHIC_TYPES = [
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"statistical", "timeline", "process", "comparison", "list",
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"geographic", "hierarchical", "anatomical", "resume", "social"
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]
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STYLE_PRESETS = [
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"corporate", "healthcare", "technology", "nature", "education",
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"marketing", "finance", "nonprofit"
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]
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PALETTE_PRESETS = ["wong", "ibm", "tol"]
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DOC_TYPES = [
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"marketing", "report", "presentation", "social", "internal", "draft", "default"
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]
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def list_options():
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"""Print available types, styles, and palettes."""
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print("""
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╔══════════════════════════════════════════════════════════════════════════════╗
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║ INFOGRAPHIC GENERATION OPTIONS ║
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╚══════════════════════════════════════════════════════════════════════════════╝
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📊 INFOGRAPHIC TYPES (--type):
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──────────────────────────────────────────────────────────────────────────────
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statistical Data-driven infographic with charts, numbers, and statistics
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timeline Chronological events or milestones
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process Step-by-step instructions or workflow
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comparison Side-by-side comparison of options
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list Tips, facts, or key points in list format
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geographic Map-based data visualization
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hierarchical Pyramid or organizational structure
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anatomical Visual metaphor explaining a system
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resume Professional skills and experience visualization
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social Social media optimized content
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🎨 STYLE PRESETS (--style):
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──────────────────────────────────────────────────────────────────────────────
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corporate Navy/gold, professional business style
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healthcare Blue/cyan, trust-inducing medical style
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technology Blue/violet, modern tech style
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nature Green/brown, environmental organic style
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education Blue/coral, friendly academic style
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marketing Coral/teal/yellow, bold vibrant style
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finance Navy/gold, conservative professional style
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nonprofit Orange/sage/sand, warm human-centered style
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🎨 COLORBLIND-SAFE PALETTES (--palette):
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──────────────────────────────────────────────────────────────────────────────
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wong Wong's palette (7 colors) - most widely recommended
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ibm IBM colorblind-safe (8 colors)
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tol Tol's qualitative (12 colors)
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📄 DOCUMENT TYPES (--doc-type):
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──────────────────────────────────────────────────────────────────────────────
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marketing 8.5/10 threshold - Marketing materials (highest quality)
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report 8.0/10 threshold - Business reports
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presentation 7.5/10 threshold - Slides and talks
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social 7.0/10 threshold - Social media content
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internal 7.0/10 threshold - Internal use
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draft 6.5/10 threshold - Working drafts (lowest quality)
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default 7.5/10 threshold - General purpose
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──────────────────────────────────────────────────────────────────────────────
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Examples:
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python generate_infographic.py "5 benefits of exercise" -o benefits.png --type list
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python generate_infographic.py "AI adoption 2020-2025" -o timeline.png --type timeline --style technology
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python generate_infographic.py "Product comparison" -o compare.png --type comparison --palette wong
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""")
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def main():
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"""Command-line interface."""
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parser = argparse.ArgumentParser(
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description="Generate infographics using Nano Banana Pro with smart iterative refinement",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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How it works:
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1. (Optional) Research phase - gather facts using Perplexity Sonar
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2. Describe your infographic in natural language
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3. Nano Banana Pro generates it automatically with:
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- Smart iteration (only regenerates if quality is below threshold)
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- Quality review by Gemini 3 Pro
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- Document-type aware quality thresholds
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- Professional-quality output
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Examples:
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# Simple list infographic
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python generate_infographic.py "5 benefits of meditation" -o benefits.png --type list
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# Corporate timeline
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python generate_infographic.py "Company history 2010-2025" -o timeline.png --type timeline --style corporate
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# Healthcare statistics with colorblind-safe colors
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python generate_infographic.py "Heart disease statistics" -o stats.png --type statistical --style healthcare --palette wong
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# Statistical infographic WITH RESEARCH for accurate data
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python generate_infographic.py "Global AI market size and growth" -o ai_market.png --type statistical --research
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# Social media infographic
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python generate_infographic.py "Save water tips" -o water.png --type social --style marketing
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# List all available options
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python generate_infographic.py --list-options
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Environment Variables:
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OPENROUTER_API_KEY Required for AI generation
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"""
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)
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parser.add_argument("prompt", nargs="?",
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help="Description of the infographic content")
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parser.add_argument("-o", "--output",
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help="Output file path")
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parser.add_argument("--type", "-t", choices=INFOGRAPHIC_TYPES,
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help="Infographic type preset")
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parser.add_argument("--style", "-s", choices=STYLE_PRESETS,
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help="Industry style preset")
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parser.add_argument("--palette", "-p", choices=PALETTE_PRESETS,
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help="Colorblind-safe palette")
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parser.add_argument("--background", "-b", default="white",
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help="Background color (default: white)")
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parser.add_argument("--doc-type", default="default", choices=DOC_TYPES,
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help="Document type for quality threshold (default: default)")
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parser.add_argument("--iterations", type=int, default=3,
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help="Maximum refinement iterations (default: 3)")
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parser.add_argument("--api-key",
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help="OpenRouter API key (or use OPENROUTER_API_KEY env var)")
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parser.add_argument("-v", "--verbose", action="store_true",
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help="Verbose output")
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parser.add_argument("--research", "-r", action="store_true",
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help="Research the topic first using Perplexity Sonar for accurate data")
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parser.add_argument("--list-options", action="store_true",
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help="List all available types, styles, and palettes")
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args = parser.parse_args()
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# Handle --list-options
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if args.list_options:
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list_options()
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return
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# Validate required arguments
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if not args.prompt:
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parser.error("prompt is required unless using --list-options")
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if not args.output:
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parser.error("--output is required")
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# Check for API key
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api_key = args.api_key or os.getenv("OPENROUTER_API_KEY")
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if not api_key:
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print("Error: OPENROUTER_API_KEY environment variable not set")
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print("\nFor AI generation, you need an OpenRouter API key.")
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print("Get one at: https://openrouter.ai/keys")
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print("\nSet it with:")
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print(" export OPENROUTER_API_KEY='your_api_key'")
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print("\nOr use --api-key flag")
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sys.exit(1)
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# Find AI generation script
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script_dir = Path(__file__).parent
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ai_script = script_dir / "generate_infographic_ai.py"
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if not ai_script.exists():
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print(f"Error: AI generation script not found: {ai_script}")
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sys.exit(1)
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# Build command
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cmd = [sys.executable, str(ai_script), args.prompt, "-o", args.output]
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if args.type:
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cmd.extend(["--type", args.type])
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if args.style:
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cmd.extend(["--style", args.style])
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if args.palette:
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cmd.extend(["--palette", args.palette])
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if args.background != "white":
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cmd.extend(["--background", args.background])
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if args.doc_type != "default":
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cmd.extend(["--doc-type", args.doc_type])
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if args.iterations != 3:
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cmd.extend(["--iterations", str(args.iterations)])
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if api_key:
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cmd.extend(["--api-key", api_key])
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if args.verbose:
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cmd.append("-v")
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if args.research:
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cmd.append("--research")
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# Execute
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try:
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result = subprocess.run(cmd, check=False)
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sys.exit(result.returncode)
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except Exception as e:
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print(f"Error executing AI generation: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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