🇺🇸English
Creative Generation Agent
Build intelligent agents that generate original creative content across multiple modalities including text, music, images, memes, and podcasts.
Overview
Creative generation combines:
- Content Models : Diffusion models, transformers, GANs
- Prompt Engineering : Guide creative output
- Style Control : Maintain artistic consistency
- Quality Assessment : Evaluate creative output
- Iteration & Refinement: Improve results
Applications
- AI music composition and arrangement
- Automated meme generation
- Podcast script and audio generation
- Creative writing assistance
- Art and image generation
- Video content creation
- Game asset generation
Quick Start
Extract the code examples and utilities from the directories:
-
Examples : See examples/ directory for complete implementations:
music_generation.py - Music generation and audio synthesismeme_generator.py - Image and text-based meme generationpodcast_producer.py - Podcast script and audio productionimage_generation.py - Diffusion-based image generationstyle_transfer.py - Neural style transfer
-
Utilities : See scripts/ directory for helper modules:
creative_quality_assessment.py - Quality evaluationaudio_effects.py - Audio effect processingcontent_moderation.py - Safety and compliance filtering
Music Generation
1. Symbolic Music Generation
Generate music as MIDI/musical notation. See examples/music_generation.py.
Key Classes:
MusicGenerationAgent - Generates melodies and full compositions- Methods:
generate_melody(), generate_full_composition(), generate_harmony()
Usage:
from examples.music_generation import MusicGenerationAgent
agent = MusicGenerationAgent()
melody = agent.generate_melody(
seed_notes=[("C4", 1), ("E4", 1), ("G4", 1)],
length=32,
temperature=0.8
)
composition = agent.generate_full_composition(style="classical", duration_bars=32)
2. Audio Synthesis
Generate audio waveforms directly. See examples/music_generation.py.
Key Classes:
AudioSynthesisAgent - Synthesizes audio from MIDI and applies effects
Usage:
from examples.music_generation import AudioSynthesisAgent
synth = AudioSynthesisAgent(sample_rate=44100)
audio = synth.synthesize_from_midi(midi_data, duration_seconds=60)
audio = synth.add_effects(audio, effect_type="reverb")
synth.save_audio(audio, "output.wav")
Meme Generation
See examples/meme_generator.py for complete implementations.
1. Image-Based Meme Generator
Generate memes by applying captions to templates.
Key Classes:
MemeGenerationAgent - Generates image-based memes with captions- Methods:
generate_meme(), generate_caption(), apply_caption_to_template()
Usage:
from examples.meme_generator import MemeGenerationAgent
agent = MemeGenerationAgent()
meme = agent.generate_meme(topic="AI agents", meme_template="drake")
meme.save("output_meme.png")
2. Text-Based Meme Generator
Generate text-only memes in various formats.
Key Classes:
TextMemeGenerator - Generates text-based memes- Methods:
generate_text_meme(), generate_joke_meme(), generate_deep_meme()
Usage:
from examples.meme_generator import TextMemeGenerator
generator = TextMemeGenerator()
joke_meme = generator.generate_text_meme(topic="Python programming", format_type="joke")
deep_meme = generator.generate_text_meme(topic="AI", format_type="deep")
Podcast Generation
See examples/podcast_producer.py for complete implementations.
1. Script Generation
Generate podcast scripts with structure and natural conversation flow.
Key Classes:
PodcastScriptGenerator - Creates scripts from topics- Methods:
generate_episode(), generate_script(), generate_content_segments(), generate_intro(), generate_outro()
Usage:
from examples.podcast_producer import PodcastScriptGenerator
generator = PodcastScriptGenerator()
episode = generator.generate_episode(
topic="Future of AI",
duration_minutes=30,
num_hosts=2
)
print(episode["script"])
2. Audio Production
Convert scripts to audio with text-to-speech and effects.
Key Classes:
PodcastAudioProducer - Produces audio from podcast scripts- Methods:
produce_podcast(), text_to_speech(), add_background_music(), add_transitions()
Usage:
from examples.podcast_producer import PodcastAudioProducer
producer = PodcastAudioProducer()
audio = producer.produce_podcast(script_text)
Image and Art Generation
See examples/image_generation.py and examples/style_transfer.py.
1. Diffusion Model Integration
Generate images from text prompts using Stable Diffusion or similar models.
Key Classes:
ImageGenerationAgent - Generates images from text prompts- Methods:
generate_image(), enhance_prompt(), generate_variations()
Usage:
from examples.image_generation import ImageGenerationAgent
agent = ImageGenerationAgent()
image = agent.generate_image(
prompt="A futuristic city with neon lights",
style="cyberpunk",
num_inference_steps=50
)
image.save("generated_image.png")
variations = agent.generate_variations(image, num_variations=4)
2. Style Transfer
Transfer artistic style from one image to another.
Key Classes:
StyleTransferAgent - Applies style transfer between images- Methods:
transfer_style(), preprocess_image(), postprocess_image()
Usage:
from examples.style_transfer import StyleTransferAgent
agent = StyleTransferAgent()
stylized = agent.transfer_style(
content_image="photo.jpg",
style_image="monet_painting.jpg"
)
Quality Assessment
See scripts/creative_quality_assessment.py for complete implementations.
1. Creative Quality Metrics
Evaluate generated content across multiple quality dimensions.
Key Classes:
CreativeQualityAssessor - Assesses quality of all content types- Methods:
assess_content_quality(), assess_music_quality(), assess_meme_quality(), assess_image_quality()
Usage:
from scripts.creative_quality_assessment import CreativeQualityAssessor
assessor = CreativeQualityAssessor()
# Assess music quality
music_assessment = assessor.assess_content_quality(audio, content_type="music")
print(f"Overall score: {music_assessment['overall_score']}")
print(f"Metrics: {music_assessment['metrics']}")
# Assess meme quality
meme_assessment = assessor.assess_content_quality(meme, content_type="meme")
# Assess image quality
image_assessment = assessor.assess_content_quality(image, content_type="image")
Best Practices
Content Generation
- ✓ Start with clear style/mood specifications
- ✓ Use temperature wisely (0.7-0.9 for creativity, 0.3-0.5 for consistency)
- ✓ Implement iterative refinement
- ✓ Maintain seed values for reproducibility
- ✓ Test with diverse prompts
Quality Control
- ✓ Assess generated content systematically (see
creative_quality_assessment.py)
- ✓ Implement human review loops
- ✓ Track quality metrics over time
- ✓ Use feedback to refine models
- ✓ Version different creative styles
Audio Processing
- ✓ Use audio effects wisely (see
audio_effects.py)
- Reverb for spatial depth
- Compression for dynamic control
- EQ for frequency balance
- Fade in/out for smooth transitions
- ✓ Monitor audio levels to prevent clipping
- ✓ Mix multiple tracks appropriately
Content Moderation
- ✓ Filter inappropriate content (see
content_moderation.py)
- ✓ Ensure copyright compliance
- ✓ Validate factual accuracy
- ✓ Check for bias in generation
- ✓ Implement safety guidelines
- ✓ Use strict mode for sensitive applications
Implementation Checklist
- Choose content modality (music, images, text, etc.)
- Select generation model/framework
- Implement prompt engineering
- Set up quality assessment metrics
- Create iterative refinement loop
- Build content moderation system
- Test generation across diverse inputs
- Optimize for speed/quality tradeoff
- Implement version control for outputs
- Document prompting strategies
Resources
Music Generation
Image Generation
Audio Synthesis
Video Generation
Weekly Installs
66
Repository
qodex-ai/ai-agent-skills
GitHub Stars
5
First Seen
Jan 22, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
claude-code46
opencode45
gemini-cli44
codex42
github-copilot40
cursor40
