feat: Add OurDigital custom skills package (10 skills)
Complete implementation of OurDigital skills with dual-platform support (Claude Desktop + Claude Code) following standardized structure. Skills created: - 01-ourdigital-brand-guide: Brand reference & style guidelines - 02-ourdigital-blog: Korean blog drafts (blog.ourdigital.org) - 03-ourdigital-journal: English essays (journal.ourdigital.org) - 04-ourdigital-research: Research prompts & workflows - 05-ourdigital-document: Notion-to-presentation pipeline - 06-ourdigital-designer: Visual/image prompt generation - 07-ourdigital-ad-manager: Ad copywriting & keyword research - 08-ourdigital-trainer: Training materials & workshop planning - 09-ourdigital-backoffice: Quotes, proposals, cost analysis - 10-ourdigital-skill-creator: Meta skill for creating new skills Features: - YAML frontmatter with "ourdigital" or "our" prefix triggers - Standardized directory structure (code/, desktop/, shared/, docs/) - Shared environment setup (_ourdigital-shared/) - Comprehensive reference documentation - Cross-skill integration support Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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#!/usr/bin/env python3
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"""
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OurDigital Mood Calibrator
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Fine-tune emotional and stylistic parameters for visual prompts
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"""
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import json
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from typing import Dict, Any
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import argparse
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class MoodCalibrator:
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def __init__(self):
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self.presets = {
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"default": {
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"contemplative_depth": 0.8,
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"cultural_fusion": 0.6,
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"technical_precision": 0.7,
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"emotional_weight": 0.5,
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"innovation_index": 0.7
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},
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"philosophical_essay": {
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"contemplative_depth": 0.95,
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"cultural_fusion": 0.7,
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"technical_precision": 0.6,
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"emotional_weight": 0.3,
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"innovation_index": 0.8
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},
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"technical_analysis": {
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"contemplative_depth": 0.6,
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"cultural_fusion": 0.4,
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"technical_precision": 0.9,
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"emotional_weight": 0.3,
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"innovation_index": 0.6
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},
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"social_commentary": {
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"contemplative_depth": 0.7,
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"cultural_fusion": 0.6,
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"technical_precision": 0.5,
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"emotional_weight": 0.7,
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"innovation_index": 0.7
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},
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"cultural_exploration": {
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"contemplative_depth": 0.8,
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"cultural_fusion": 0.9,
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"technical_precision": 0.4,
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"emotional_weight": 0.6,
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"innovation_index": 0.8
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}
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}
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self.parameter_descriptions = {
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"contemplative_depth": "Level of abstraction (0=literal, 1=highly abstract)",
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"cultural_fusion": "Balance of Korean-Western aesthetics (0=Western only, 1=Korean dominant)",
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"technical_precision": "Clean geometric vs organic forms (0=organic, 1=geometric)",
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"emotional_weight": "Mood intensity (0=neutral, 1=heavy atmosphere)",
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"innovation_index": "Traditional vs experimental approach (0=traditional, 1=experimental)"
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}
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def calibrate(self, preset: str = "default", **overrides) -> Dict[str, float]:
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"""
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Get calibrated mood parameters
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Args:
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preset: Base preset to use
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**overrides: Specific parameter overrides
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Returns:
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Dictionary of calibrated parameters
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"""
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params = self.presets.get(preset, self.presets["default"]).copy()
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# Apply overrides
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for key, value in overrides.items():
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if key in params:
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params[key] = max(0, min(1, value)) # Clamp to 0-1
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return params
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def generate_modifier_text(self, params: Dict[str, float]) -> str:
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"""
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Generate text modifiers based on parameters
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Args:
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params: Dictionary of mood parameters
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Returns:
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Text description for prompt modification
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"""
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modifiers = []
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# Contemplative depth
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if params["contemplative_depth"] > 0.8:
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modifiers.append("highly abstract and philosophical")
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elif params["contemplative_depth"] > 0.6:
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modifiers.append("semi-abstract with symbolic elements")
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else:
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modifiers.append("grounded with recognizable forms")
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# Cultural fusion
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if params["cultural_fusion"] > 0.7:
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modifiers.append("strong Korean aesthetic influence")
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elif params["cultural_fusion"] > 0.4:
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modifiers.append("balanced East-West aesthetic fusion")
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else:
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modifiers.append("Western minimalist approach")
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# Technical precision
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if params["technical_precision"] > 0.7:
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modifiers.append("precise geometric construction")
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elif params["technical_precision"] > 0.4:
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modifiers.append("blend of geometric and organic")
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else:
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modifiers.append("flowing organic forms")
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# Emotional weight
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if params["emotional_weight"] > 0.7:
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modifiers.append("heavy, contemplative atmosphere")
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elif params["emotional_weight"] > 0.4:
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modifiers.append("balanced emotional tone")
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else:
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modifiers.append("light, open feeling")
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# Innovation index
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if params["innovation_index"] > 0.7:
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modifiers.append("experimental and avant-garde")
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elif params["innovation_index"] > 0.4:
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modifiers.append("contemporary with classic elements")
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else:
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modifiers.append("timeless and traditional")
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return ", ".join(modifiers)
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def suggest_color_temperature(self, params: Dict[str, float]) -> str:
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"""
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Suggest color temperature based on parameters
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Args:
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params: Dictionary of mood parameters
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Returns:
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Color temperature suggestion
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"""
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emotional = params.get("emotional_weight", 0.5)
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technical = params.get("technical_precision", 0.7)
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if emotional > 0.6:
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if technical > 0.6:
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return "Cool palette with strategic warm accents"
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else:
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return "Warm, earthy tones with depth"
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else:
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if technical > 0.6:
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return "Neutral grays with single accent color"
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else:
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return "Soft, desaturated natural colors"
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def export_for_prompt(self, params: Dict[str, float]) -> Dict[str, Any]:
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"""
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Export parameters in a format ready for prompt generation
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Args:
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params: Dictionary of mood parameters
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Returns:
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Formatted export for prompt generation
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"""
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return {
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"parameters": params,
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"modifiers": self.generate_modifier_text(params),
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"color_temperature": self.suggest_color_temperature(params),
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"abstraction_level": params["contemplative_depth"],
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"cultural_balance": params["cultural_fusion"]
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}
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def main():
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parser = argparse.ArgumentParser(
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description="Calibrate mood parameters for OurDigital visual prompts"
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)
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parser.add_argument(
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"--preset",
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choices=["default", "philosophical_essay", "technical_analysis",
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"social_commentary", "cultural_exploration"],
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default="default",
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help="Base preset to use"
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)
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parser.add_argument(
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"--contemplative-depth",
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type=float,
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help="Override contemplative depth (0-1)"
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)
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parser.add_argument(
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"--cultural-fusion",
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type=float,
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help="Override cultural fusion (0-1)"
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)
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parser.add_argument(
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"--technical-precision",
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type=float,
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help="Override technical precision (0-1)"
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)
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parser.add_argument(
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"--emotional-weight",
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type=float,
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help="Override emotional weight (0-1)"
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)
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parser.add_argument(
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"--innovation-index",
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type=float,
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help="Override innovation index (0-1)"
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)
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parser.add_argument(
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"--export",
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action="store_true",
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help="Export full configuration for prompt generation"
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)
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args = parser.parse_args()
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calibrator = MoodCalibrator()
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# Build overrides
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overrides = {}
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if args.contemplative_depth is not None:
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overrides["contemplative_depth"] = args.contemplative_depth
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if args.cultural_fusion is not None:
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overrides["cultural_fusion"] = args.cultural_fusion
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if args.technical_precision is not None:
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overrides["technical_precision"] = args.technical_precision
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if args.emotional_weight is not None:
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overrides["emotional_weight"] = args.emotional_weight
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if args.innovation_index is not None:
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overrides["innovation_index"] = args.innovation_index
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# Calibrate
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params = calibrator.calibrate(args.preset, **overrides)
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if args.export:
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export = calibrator.export_for_prompt(params)
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print(json.dumps(export, indent=2, ensure_ascii=False))
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else:
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print(f"Calibrated Parameters for '{args.preset}':")
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print("-" * 50)
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for key, value in params.items():
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desc = calibrator.parameter_descriptions[key]
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print(f"{key}: {value:.2f} - {desc}")
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print("-" * 50)
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print(f"Modifiers: {calibrator.generate_modifier_text(params)}")
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print(f"Color Temperature: {calibrator.suggest_color_temperature(params)}")
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if __name__ == "__main__":
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main()
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