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arcads-ai-video-agent

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UpdatedJuly 8, 2026 at 11:14

Create AI marketing videos and images using Arcads API with Seedance 2.0, Sora 2, Veo 3.1, Kling 3.0, Nano Banana, and 37 static Meta ad templates

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Aradotso/claude-code-skills

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SKILL.md

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Arcads AI Video Agent

Skill by ara.so — Claude Code Skills collection.

This skill enables AI agents to create marketing videos and images using the Arcads platform. It supports the full creative stack: Seedance 2.0 (flagship video), Sora 2, Veo 3.1, Kling 3.0, Grok Video, Nano Banana 2/Pro/Edit, ChatGPT Image 2, OmniHuman, and Audio-driven models, plus 37 validated static Meta image-ad templates and multi-step pipelines for Pixar-style and claymation animated ads.

Prerequisites

Python 3.10+
Arcads API key from app.arcads.ai/settings/api
Optional tools for advanced workflows:
- ffmpeg (video stitching, chroma-key)
- jq (JSON parsing in bash scripts)
- Node.js + npx hyperframes (caption burn-in)
- openai-whisper (transcription: pip install openai-whisper)

Installation

# Clone the repository
git clone https://github.com/krusemediallc/arcads-claude-code.git
cd arcads-claude-code

# Run setup script
./scripts/setup.sh

The setup script will:

Prompt for your Arcads API key
Create .env file with ARCADS_API_KEY=your_key_here
Verify API connection
Create MASTER_CONTEXT.md workspace file

Manual setup (if you skip the script):

# Create .env file
echo "ARCADS_API_KEY=your_api_key_here" > .env

Core API Patterns

All Arcads API calls follow this pattern:

import os
import requests
import time
from dotenv import load_dotenv

load_dotenv()

BASE_URL = "https://api.arcads.ai"
API_KEY = os.getenv("ARCADS_API_KEY")

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

Standard Generation Flow

Submit job → get jobId
Poll status until completed or failed
Download result

def submit_job(endpoint, payload):
    """Submit a generation job to Arcads API"""
    response = requests.post(
        f"{BASE_URL}{endpoint}",
        headers=headers,
        json=payload
    )
    response.raise_for_status()
    return response.json()["jobId"]

def poll_status(job_id, timeout=600, interval=10):
    """Poll job status until complete"""
    start_time = time.time()
    while time.time() - start_time < timeout:
        response = requests.get(
            f"{BASE_URL}/v1/jobs/{job_id}",
            headers=headers
        )
        data = response.json()
        status = data["status"]
        
        if status == "completed":
            return data["result"]
        elif status == "failed":
            raise Exception(f"Job failed: {data.get('error')}")
        
        time.sleep(interval)
    
    raise TimeoutError(f"Job {job_id} timed out after {timeout}s")

def download_file(url, output_path):
    """Download generated asset"""
    response = requests.get(url, stream=True)
    response.raise_for_status()
    with open(output_path, 'wb') as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)

Video Generation

Seedance 2.0 (Flagship Model)

Best for: UGC videos, product reveals, hero shots, lookbooks, feature demos (4-15s)

def generate_seedance_video(prompt, duration=10, style="ugc"):
    """Generate Seedance 2.0 video with prompt engineering"""
    
    # UGC formula (9-layer structure)
    if style == "ugc":
        full_prompt = f"""
[PERSON] Woman, late 20s, natural makeup, casual kitchen setting
[OPENER] Direct camera eye contact, authentic energy, "Hey guys!"
[HOOK] Attention-grabbing statement about {prompt}
[PROBLEM] Relatable pain point, conversational tone
[SOLUTION] Product introduction, natural hand gestures
[DEMO] Show product in use, realistic interaction
[BENEFIT] Key value prop, maintains eye contact
[SOCIAL_PROOF] Brief testimonial feel
[CTA] Natural call to action, warm energy
[STYLE] iPhone selfie aesthetic, natural lighting, slight camera shake
"""
    else:
        full_prompt = prompt
    
    payload = {
        "prompt": full_prompt,
        "duration": duration,
        "aspectRatio": "9:16"  # or "16:9", "1:1"
    }
    
    job_id = submit_job("/v2/videos/generate", payload)
    print(f"Seedance job submitted: {job_id}")
    
    result = poll_status(job_id)
    video_url = result["videoUrl"]
    
    download_file(video_url, f"output/seedance_{job_id}.mp4")
    return video_url

Example usage:

# UGC product review
video = generate_seedance_video(
    "skin serum that reduced dark circles in 2 weeks",
    duration=12,
    style="ugc"
)

# Premium product reveal
payload = {
    "prompt": """
[SCENE] Dark void, single spotlight
[PRODUCT] Luxury perfume bottle materializes
[MOTION] Slow 360° rotation, golden light rays
[TEXT] Overlay: "Crafted for those who dare"
[AESTHETIC] High-contrast, cinematic, no human presence
""",
    "duration": 8,
    "aspectRatio": "9:16"
}
job_id = submit_job("/v2/videos/generate", payload)

Veo 3.1 (Image-to-Video with Dialogue)

Best for: Animating Nano Banana stills into talking UGC videos

def animate_with_veo(image_path, dialogue, duration=8):
    """Animate a still image with Veo 3.1 + dialogue"""
    
    import base64
    
    # Load and encode starting frame
    with open(image_path, "rb") as f:
        image_b64 = base64.b64encode(f.read()).decode()
    
    payload = {
        "startFrame": image_b64,
        "prompt": f"""
Natural human motion, authentic energy, person speaks:
"{dialogue}"
Maintain character likeness from starting frame.
iPhone selfie aesthetic, slight head movement, natural eye contact.
""",
        "dialogue": dialogue,  # MANDATORY for dialogue videos
        "duration": duration,
        "aspectRatio": "9:16"
    }
    
    job_id = submit_job("/v1/veo3-1/video", payload)
    result = poll_status(job_id, timeout=900)  # Veo takes longer
    
    download_file(result["videoUrl"], f"output/veo_{job_id}.mp4")
    return result["videoUrl"]

Sora 2 (Text-to-Video, Longer Durations)

Best for: Cinematic scenes, B-roll, up to 20s

def generate_sora_video(prompt, duration=16):
    """Generate Sora 2 video (supports longer durations)"""
    
    payload = {
        "prompt": prompt,
        "duration": duration,  # Auto-calculated from word count if omitted
        "aspectRatio": "16:9"
    }
    
    # Optional: add style reference image
    # payload["styleReference"] = base64_encoded_image
    
    job_id = submit_job("/v1/sora2/video", payload)
    result = poll_status(job_id, timeout=1200)
    
    download_file(result["videoUrl"], f"output/sora_{job_id}.mp4")
    return result["videoUrl"]

Kling 3.0 (B-Roll & Scenes)

Best for: Scene generation, environmental b-roll

def generate_broll(scene_description):
    """Generate b-roll clip with Kling 3.0"""
    
    payload = {
        "prompt": scene_description,
        "duration": 5
    }
    
    job_id = submit_job("/v1/b-roll", payload)
    result = poll_status(job_id)
    
    download_file(result["videoUrl"], f"output/broll_{job_id}.mp4")
    return result["videoUrl"]

# Example
generate_broll("Golden hour beach waves, slow motion, cinematic")

Image Generation

Nano Banana (Character Creation & Product Stills)

Best for: AI influencers, UGC stills, photoreal product shots

def create_nano_banana_image(prompt, reference_images=None, model="nano-banana-2"):
    """
    Generate image with Nano Banana
    model options: "nano-banana-2" (default), "nano-banana" (Pro), "nano-banana-edit"
    """
    
    payload = {
        "prompt": prompt,
        "model": model,
        "aspectRatio": "9:16"
    }
    
    # Add reference images for character consistency
    if reference_images:
        import base64
        refs = []
        for img_path in reference_images:
            with open(img_path, "rb") as f:
                refs.append(base64.b64encode(f.read()).decode())
        payload["referenceImages"] = refs
    
    job_id = submit_job("/v1/nano-banana/image", payload)
    result = poll_status(job_id)
    
    download_file(result["imageUrl"], f"output/nano_{job_id}.png")
    return result["imageUrl"]

Example: Create AI Influencer (10-image character sheet)

def create_ai_influencer(description):
    """Generate 10-angle character sheet for AI influencer"""
    
    # Step 1: Generate hero front portrait
    hero_prompt = f"""
{description}
Front-facing portrait, natural expression, golden hour lighting.
Photoreal skin texture, freckles, pores visible.
Soft focus background, kitchen setting.
"""
    
    hero_url = create_nano_banana_image(hero_prompt)
    print(f"Hero portrait: {hero_url}")
    print("Review and approve hero before generating remaining 9 angles.")
    
    # Step 2: Generate 9 additional angles using hero as reference
    angles = [
        "3/4 view left, slight smile",
        "3/4 view right, natural expression",
        "Profile left, looking away",
        "Profile right, looking forward",
        "Close-up, eyes focused on camera",
        "Full body, standing casual pose",
        "Laughing, animated expression",
        "Serious expression, direct gaze",
        "Lifestyle shot, holding coffee mug"
    ]
    
    results = []
    for angle in angles:
        prompt = f"{description}\n{angle}\nMaintain exact character likeness."
        url = create_nano_banana_image(
            prompt,
            reference_images=["output/hero_portrait.png"],
            model="nano-banana"  # Use Pro for tighter identity lock
        )
        results.append(url)
    
    return results

ChatGPT Image 2 (Typography & UI-Heavy Ads)

Best for: Apple Notes lists, fake Slack threads, editorial layouts, comparison tables

def generate_chatgpt_image(prompt, aspect_ratio="1:1"):
    """Generate image with ChatGPT Image 2 (gpt-image-2)"""
    
    payload = {
        "prompt": prompt,
        "model": "gpt-image-2",
        "aspectRatio": aspect_ratio  # "1:1", "4:5", "16:9"
    }
    
    job_id = submit_job("/v1/image/generate", payload)
    result = poll_status(job_id)
    
    download_file(result["imageUrl"], f"output/chatgpt_{job_id}.png")
    return result["imageUrl"]

Static Meta Image Ad Templates (37-Template Library)

The repo includes 37 validated prompt templates for static Meta image ads. Use the specialized skills:

# Apple Notes-style list ad
def generate_apple_notes_ad(product, benefits):
    """Generate Apple Notes-style ad (ChatGPT Image 2)"""
    
    prompt = f"""
iPhone Notes app interface, cream background.
Title: "why i switched to {product}"
Bulleted list:
{chr(10).join(f'• {b}' for b in benefits)}

Footer: handwritten-style signature.
Clean iOS typography, authentic spacing, no visible phone edges.
"""
    
    return generate_chatgpt_image(prompt, aspect_ratio="4:5")

# Photoreal UGC selfie ad
def generate_ugc_selfie_ad(influencer_ref, product_ref):
    """Generate UGC selfie with product (Nano Banana)"""
    
    prompt = """
iPhone selfie, natural bedroom lighting.
[influencer] holding [product], casual smile.
Authentic skin texture, slight motion blur.
Visible pores, flyaway hairs, iPhone camera imperfections.
Product visible and recognizable, natural hand position.
"""
    
    return create_nano_banana_image(
        prompt,
        reference_images=[influencer_ref, product_ref],
        model="nano-banana-2"
    )

Template categories (see shared/skills/image-ad-prompting/library/ for full list):

UI/App Mimicry: Apple Notes, Slack threads, iMessage screenshots, ChatGPT conversations, Google search results
Editorial: Forbes hero, magazine cover, billboard, museum exhibit
Comparison: Feature tables, before/after splits, versus cards
Lifestyle: Sticky-note flatlays, founder letter, dating-app card, weather forecast UI

Multi-Step Animated Ad Pipelines

Pixar-Style 3D Animated Ad

8-beat story arc → ChatGPT Image 2 storyboard → Seedance 2.0 i2v per beat → ffmpeg stitch + captions

def generate_pixar_ad(product, story_arc):
    """
    Generate Pixar-style animated ad
    story_arc: list of 8 beat descriptions
    """
    
    # Step 1: Lock cast sheet (character designs)
    cast_prompt = """
3D Pixar style character sheet.
Anthropomorphized [product mascot], friendly expression.
Multiple angles: front, 3/4, profile, back.
Consistent lighting, white background.
"""
    
    cast_url = generate_chatgpt_image(cast_prompt)
    print(f"Cast sheet: {cast_url}")
    
    # Step 2: Generate storyboard stills (8 beats)
    stills = []
    prev_frame = None
    
    for i, beat in enumerate(story_arc):
        prompt = f"""
3D Pixar animation style frame.
Beat {i+1}: {beat}
Maintain character consistency from cast sheet.
Cinematic lighting, shallow depth of field.
"""
        
        refs = [cast_url]
        if prev_frame:
            refs.append(prev_frame)  # Max 5 references total
        
        # Use ChatGPT Image 2 for sequential stills
        job_id = submit_job("/v1/image/generate", {
            "prompt": prompt,
            "model": "gpt-image-2",
            "referenceImages": refs,
            "aspectRatio": "16:9"
        })
        
        result = poll_status(job_id)
        still_path = f"output/beat_{i+1}.png"
        download_file(result["imageUrl"], still_path)
        
        stills.append(still_path)
        prev_frame = result["imageUrl"]
    
    # Step 3: Animate each still with Seedance 2.0 i2v
    videos = []
    for i, still in enumerate(stills):
        video = animate_still_with_seedance(still, story_arc[i])
        videos.append(video)
    
    # Step 4: Stitch with ffmpeg
    stitch_videos(videos, "output/pixar_ad_final.mp4")
    
    # Step 5: Burn captions (optional)
    # burn_captions("output/pixar_ad_final.mp4", "output/pixar_ad_captioned.mp4")
    
    return "output/pixar_ad_final.mp4"

def animate_still_with_seedance(image_path, motion_description):
    """Animate a still image with Seedance 2.0 i2v"""
    
    import base64
    with open(image_path, "rb") as f:
        img_b64 = base64.b64encode(f.read()).decode()
    
    payload = {
        "startFrame": img_b64,
        "prompt": f"{motion_description}\nMaintain scene composition, add subtle motion.",
        "duration": 3
    }
    
    job_id = submit_job("/v2/videos/generate", payload)
    result = poll_status(job_id)
    
    output_path = f"output/animated_{int(time.time())}.mp4"
    download_file(result["videoUrl"], output_path)
    return output_path

def stitch_videos(video_paths, output_path):
    """Stitch multiple videos with ffmpeg"""
    
    import subprocess
    
    # Create concat file
    with open("concat_list.txt", "w") as f:
        for path in video_paths:
            f.write(f"file '{path}'\n")
    
    subprocess.run([
        "ffmpeg", "-f", "concat", "-safe", "0",
        "-i", "concat_list.txt",
        "-c", "copy", output_path
    ], check=True)

Caption Burn-In Workflow

def burn_captions(video_path, output_path):
    """Transcribe and burn captions onto video"""
    
    import subprocess
    import whisper
    
    # Step 1: Transcribe with Whisper
    model = whisper.load_model("medium.en")
    result = model.transcribe(video_path, word_timestamps=True)
    
    # Step 2: Group word-level timestamps into phrases
    phrases = group_into_phrases(result["segments"])
    
    # Step 3: Generate caption JSON for HyperFrames
    captions_json = {
        "captions": [
            {
                "start": p["start"],
                "end": p["end"],
                "text": p["text"]
            }
            for p in phrases
        ]
    }
    
    with open("captions.json", "w") as f:
        import json
        json.dump(captions_json, f)
    
    # Step 4: Render with HyperFrames
    subprocess.run([
        "npx", "hyperframes",
        "--input", video_path,
        "--captions", "captions.json",
        "--output", output_path,
        "--style", "modern"  # or "classic", "minimal"
    ], check=True)

def group_into_phrases(segments, max_words=5):
    """Group word-level timestamps into reading phrases"""
    
    phrases = []
    current_phrase = {"words": [], "start": 0, "end": 0}
    
    for segment in segments:
        for word_info in segment.get("words", []):
            current_phrase["words"].append(word_info["word"])
            
            if not current_phrase["start"]:
                current_phrase["start"] = word_info["start"]
            
            current_phrase["end"] = word_info["end"]
            
            if len(current_phrase["words"]) >= max_words:
                phrases.append({
                    "text": " ".join(current_phrase["words"]),
                    "start": current_phrase["start"],
                    "end": current_phrase["end"]
                })
                current_phrase = {"words": [], "start": 0, "end": 0}
    
    # Add remaining words
    if current_phrase["words"]:
        phrases.append({
            "text": " ".join(current_phrase["words"]),
            "start": current_phrase["start"],
            "end": current_phrase["end"]
        })
    
    return phrases

Configuration

All configuration is in .env:

ARCADS_API_KEY=your_api_key_here

# Optional: default models
DEFAULT_VIDEO_MODEL=seedance-2
DEFAULT_IMAGE_MODEL=nano-banana-2

# Optional: output directories
OUTPUT_DIR=output
REFERENCES_DIR=references

# Optional: API timeouts
JOB_TIMEOUT=600
POLL_INTERVAL=10

Common Workflows

Full UGC Video Workflow (Still → Video)

def create_ugc_video_from_scratch(influencer_desc, product_ref, script):
    """Complete UGC workflow: character → still → video"""
    
    # Step 1: Create AI influencer if needed
    influencer_refs = create_ai_influencer(influencer_desc)
    hero_ref = influencer_refs[0]
    
    # Step 2: Generate UGC still with product
    still_prompt = f"""
{influencer_desc}
iPhone selfie, bedroom setting, natural lighting.
Holding {product_ref}, casual smile, looking at camera.
Authentic skin texture, slight motion blur.
"""
    
    still_url = create_nano_banana_image(
        still_prompt,
        reference_images=[hero_ref, product_ref]
    )
    
    download_file(still_url, "temp_still.png")
    
    # Step 3: Animate with Veo 3.1 + dialogue
    video_url = animate_with_veo("temp_still.png", script, duration=12)
    
    return video_url

Batch Video Generation

def batch_generate_videos(prompts, model="seedance-2"):
    """Submit multiple video jobs in parallel"""
    
    job_ids = []
    
    # Submit all jobs
    for prompt in prompts:
        payload = {
            "prompt": prompt,
            "duration": 10,
            "aspectRatio": "9:16"
        }
        job_id = submit_job("/v2/videos/generate", payload)
        job_ids.append(job_id)
        print(f"Submitted: {job_id}")
    
    # Poll all jobs
    results = []
    for job_id in job_ids:
        try:
            result = poll_status(job_id, timeout=900)
            video_path = f"output/batch_{job_id}.mp4"
            download_file(result["videoUrl"], video_path)
            results.append(video_path)
        except Exception as e:
            print(f"Job {job_id} failed: {e}")
    
    return results

Cost Estimation

def estimate_cost(model, duration=10):
    """Estimate generation cost before submitting"""
    
    costs = {
        "seedance-2": 0.10 * (duration / 10),  # $0.10 per 10s
        "veo-3-1": 0.15 * (duration / 10),
        "sora-2": 0.20 * (duration / 10),
        "kling-3": 0.08 * (duration / 10),
        "nano-banana-2": 0.05,  # per image
        "nano-banana": 0.08,  # Pro
        "gpt-image-2": 0.04
    }
    
    return costs.get(model, 0.10)

# Confirm before generating
cost = estimate_cost("seedance-2", duration=12)
print(f"Estimated cost: ${cost:.2f}")
response = input("Proceed? (y/n): ")
if response.lower() == "y":
    generate_seedance_video(prompt, duration=12)

Troubleshooting

API Key Issues

def verify_api_connection():
    """Test API key validity"""
    
    try:
        response = requests.get(
            f"{BASE_URL}/v1/account",
            headers=headers
        )
        response.raise_for_status()
        print("✓ API connection successful")
        print(f"Account: {response.json()['email']}")
        return True
    except requests.exceptions.HTTPError as e:
        if e.response.status_code == 401:
            print("✗ Invalid API key")
            print("Get your key: https://app.arcads.ai/settings/api")
        else:
            print(f"✗ API error: {e}")
        return False

Job Timeout Issues

If jobs are timing out (Veo/Sora can take 15+ minutes):

# Increase timeout for slower models
result = poll_status(job_id, timeout=1800, interval=30)  # 30min, check every 30s

Character Consistency Issues

For better character lock across multiple images:

# Use Nano Banana Pro instead of default
create_nano_banana_image(
    prompt,
    reference_images=[hero_ref],
    model="nano-banana"  # Pro model (Gemini 3 Pro Image)
)

# Provide 3-5 reference images from different angles
refs = [
    "references/influencers/sofia_front.png",
    "references/influencers/sofia_3_4.png",
    "references/influencers/sofia_profile.png"
]

Video Quality Issues

If Seedance videos lack motion or feel static:

# Add explicit motion cues to prompt
prompt = """
[SCENE] Woman in kitchen, dynamic camera movement
[MOTION] Head turns toward camera, hand gestures, product raise
[ACTION] Walks forward 2 steps, smiles broadly
[ENERGY] Animated, enthusiastic, constant subtle movement
"""

ffmpeg Not Found

# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt install ffmpeg

# Verify installation
ffmpeg -version

Advanced: Publishing to Meta Ads

The repo includes a meta-ad-builder skill for publishing generated images as paused Meta ads:

# Install dependencies first
# pip install -r shared/skills/meta-ad-builder/scripts/requirements.txt

def publish_to_meta(image_path, ad_account_id, campaign_name):
    """Publish generated image as paused Meta ad"""
    
    import subprocess
    
    # Set required env vars
    os.environ["META_ACCESS_TOKEN"] = os.getenv("META_ACCESS_TOKEN")
    os.environ["AD_ACCOUNT_ID"] = ad_account_id
    
    # Run meta-ad-builder script
    subprocess.run([
        "python",
        "shared/skills/meta-ad-builder/scripts/create_ad.py",
        "--image", image_path,
        "--campaign", campaign_name,
        "--status", "PAUSED"
    ], check=True)

Requires META_ACCESS_TOKEN and AD_ACCOUNT_ID in .env.

File Organization

The agent auto-organizes outputs:

project/
├── output/                    # Generated assets
│   ├── seedance_*.mp4
│   ├── nano_*.png
│   └── pixar_ad_final.mp4
├── references/                # Reference libraries
│   ├── influencers/          # AI character sheets
│   ├── products/             # Product photos
│   └── aesthetics/           # Style references
├── .env                       # API keys (gitignored)
└── MASTER_CONTEXT.md         # Project workspace file

Prompt Engineering Tips

Seedance 2.0 UGC Formula

Use the 9-layer structure for authentic UGC:

[PERSON] demographics + appearance
[OPENER] eye contact + greeting
[HOOK] attention-grabbing statement
[PROBLEM] relatable pain point
[SOLUTION] product intro
[DEMO] product in action
[BENEFIT] key value prop
[SOCIAL_PROOF] testimonial feel
[CTA] natural call to action
[STYLE] iPhone aesthetic + lighting

Nano Banana Character Consistency

For AI influencers that look the same across 100+ images:

Generate hero portrait with detailed description
Use hero as referenceImages[0] for all subsequent generations
Use Nano Banana Pro (model="nano-banana") for tighter lock
Include "maintain exact character likeness" in every prompt
Lock lighting/background when possible (e.g., "golden hour kitchen" across all shots)

Veo 3.1 Dialogue Best Practices

Always include dialogue field (mandatory for talking videos)
Keep dialogue under 20 words for 8s clips (~2.5 words/sec)
Prompt for "natural head movement, slight pauses, eye contact breaks"
Starting frame should show person's face clearly (not profile/back)

API Endpoints Reference

# Video generation
"/v2/videos/generate"           # Seedance 2.0, Sora 2, Grok Video
"/v1/veo3-1/video"              # Veo 3.1 with startFrame + dialogue
"/v1/sora2/video"               # Sora 2 text-to-video
"/v1/sora2/remix/video"         # Sora 2 remix
"/v1/b-roll"                    # Kling 3.0 b-roll
"/v1/scene"                     # Kling 3.0 scene
"/v1/omnihuman"                 # OmniHuman avatar
"/v1/audio-driven"              # Audio-driven lip-sync

# Image generation
"/v1/nano-banana/image"         # Nano Banana 2/Pro/Edit
"/v1/image/generate"            # ChatGPT Image 2

# Job management
"/v1/jobs/{jobId}"              # Poll job status
"/v1/account"                   # Verify API key

Cost Management

Always confirm costs before batch operations:

def confirm_batch_cost(num_videos, model="seedance-2", duration=10):
    """Confirm cost before batch generation

name	arcads-ai-video-agent
description	Create AI marketing videos and images using Arcads API with Seedance 2.0, Sora 2, Veo 3.1, Kling 3.0, Nano Banana, and 37 static Meta ad templates
triggers	["generate an arcads video","create ai marketing video with seedance","make a nano banana image ad","generate ugc video with arcads","create pixar style animated ad","make meta image ad with arcads","use arcads api for video generation","create ai influencer character sheet"]

Arcads AI Video Agent

Skill by ara.so — Claude Code Skills collection.

Prerequisites

Python 3.10+
Arcads API key from app.arcads.ai/settings/api
Optional tools for advanced workflows:
- ffmpeg (video stitching, chroma-key)
- jq (JSON parsing in bash scripts)
- Node.js + npx hyperframes (caption burn-in)
- openai-whisper (transcription: pip install openai-whisper)

Installation

# Clone the repository
git clone https://github.com/krusemediallc/arcads-claude-code.git
cd arcads-claude-code

# Run setup script
./scripts/setup.sh

The setup script will:

Prompt for your Arcads API key
Create .env file with ARCADS_API_KEY=your_key_here
Verify API connection
Create MASTER_CONTEXT.md workspace file

Manual setup (if you skip the script):

# Create .env file
echo "ARCADS_API_KEY=your_api_key_here" > .env

Core API Patterns

All Arcads API calls follow this pattern:

import os
import requests
import time
from dotenv import load_dotenv

load_dotenv()

BASE_URL = "https://api.arcads.ai"
API_KEY = os.getenv("ARCADS_API_KEY")

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

Standard Generation Flow

Submit job → get jobId
Poll status until completed or failed
Download result

def submit_job(endpoint, payload):
    """Submit a generation job to Arcads API"""
    response = requests.post(
        f"{BASE_URL}{endpoint}",
        headers=headers,
        json=payload
    )
    response.raise_for_status()
    return response.json()["jobId"]

def poll_status(job_id, timeout=600, interval=10):
    """Poll job status until complete"""
    start_time = time.time()
    while time.time() - start_time < timeout:
        response = requests.get(
            f"{BASE_URL}/v1/jobs/{job_id}",
            headers=headers
        )
        data = response.json()
        status = data["status"]
        
        if status == "completed":
            return data["result"]
        elif status == "failed":
            raise Exception(f"Job failed: {data.get('error')}")
        
        time.sleep(interval)
    
    raise TimeoutError(f"Job {job_id} timed out after {timeout}s")

def download_file(url, output_path):
    """Download generated asset"""
    response = requests.get(url, stream=True)
    response.raise_for_status()
    with open(output_path, 'wb') as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)

Video Generation

Seedance 2.0 (Flagship Model)

Best for: UGC videos, product reveals, hero shots, lookbooks, feature demos (4-15s)

def generate_seedance_video(prompt, duration=10, style="ugc"):
    """Generate Seedance 2.0 video with prompt engineering"""
    
    # UGC formula (9-layer structure)
    if style == "ugc":
        full_prompt = f"""
[PERSON] Woman, late 20s, natural makeup, casual kitchen setting
[OPENER] Direct camera eye contact, authentic energy, "Hey guys!"
[HOOK] Attention-grabbing statement about {prompt}
[PROBLEM] Relatable pain point, conversational tone
[SOLUTION] Product introduction, natural hand gestures
[DEMO] Show product in use, realistic interaction
[BENEFIT] Key value prop, maintains eye contact
[SOCIAL_PROOF] Brief testimonial feel
[CTA] Natural call to action, warm energy
[STYLE] iPhone selfie aesthetic, natural lighting, slight camera shake
"""
    else:
        full_prompt = prompt
    
    payload = {
        "prompt": full_prompt,
        "duration": duration,
        "aspectRatio": "9:16"  # or "16:9", "1:1"
    }
    
    job_id = submit_job("/v2/videos/generate", payload)
    print(f"Seedance job submitted: {job_id}")
    
    result = poll_status(job_id)
    video_url = result["videoUrl"]
    
    download_file(video_url, f"output/seedance_{job_id}.mp4")
    return video_url

Example usage:

# UGC product review
video = generate_seedance_video(
    "skin serum that reduced dark circles in 2 weeks",
    duration=12,
    style="ugc"
)

# Premium product reveal
payload = {
    "prompt": """
[SCENE] Dark void, single spotlight
[PRODUCT] Luxury perfume bottle materializes
[MOTION] Slow 360° rotation, golden light rays
[TEXT] Overlay: "Crafted for those who dare"
[AESTHETIC] High-contrast, cinematic, no human presence
""",
    "duration": 8,
    "aspectRatio": "9:16"
}
job_id = submit_job("/v2/videos/generate", payload)

Veo 3.1 (Image-to-Video with Dialogue)

Best for: Animating Nano Banana stills into talking UGC videos

def animate_with_veo(image_path, dialogue, duration=8):
    """Animate a still image with Veo 3.1 + dialogue"""
    
    import base64
    
    # Load and encode starting frame
    with open(image_path, "rb") as f:
        image_b64 = base64.b64encode(f.read()).decode()
    
    payload = {
        "startFrame": image_b64,
        "prompt": f"""
Natural human motion, authentic energy, person speaks:
"{dialogue}"
Maintain character likeness from starting frame.
iPhone selfie aesthetic, slight head movement, natural eye contact.
""",
        "dialogue": dialogue,  # MANDATORY for dialogue videos
        "duration": duration,
        "aspectRatio": "9:16"
    }
    
    job_id = submit_job("/v1/veo3-1/video", payload)
    result = poll_status(job_id, timeout=900)  # Veo takes longer
    
    download_file(result["videoUrl"], f"output/veo_{job_id}.mp4")
    return result["videoUrl"]

Sora 2 (Text-to-Video, Longer Durations)

Best for: Cinematic scenes, B-roll, up to 20s

def generate_sora_video(prompt, duration=16):
    """Generate Sora 2 video (supports longer durations)"""
    
    payload = {
        "prompt": prompt,
        "duration": duration,  # Auto-calculated from word count if omitted
        "aspectRatio": "16:9"
    }
    
    # Optional: add style reference image
    # payload["styleReference"] = base64_encoded_image
    
    job_id = submit_job("/v1/sora2/video", payload)
    result = poll_status(job_id, timeout=1200)
    
    download_file(result["videoUrl"], f"output/sora_{job_id}.mp4")
    return result["videoUrl"]

Kling 3.0 (B-Roll & Scenes)

Best for: Scene generation, environmental b-roll

def generate_broll(scene_description):
    """Generate b-roll clip with Kling 3.0"""
    
    payload = {
        "prompt": scene_description,
        "duration": 5
    }
    
    job_id = submit_job("/v1/b-roll", payload)
    result = poll_status(job_id)
    
    download_file(result["videoUrl"], f"output/broll_{job_id}.mp4")
    return result["videoUrl"]

# Example
generate_broll("Golden hour beach waves, slow motion, cinematic")

Image Generation

Nano Banana (Character Creation & Product Stills)

Best for: AI influencers, UGC stills, photoreal product shots

def create_nano_banana_image(prompt, reference_images=None, model="nano-banana-2"):
    """
    Generate image with Nano Banana
    model options: "nano-banana-2" (default), "nano-banana" (Pro), "nano-banana-edit"
    """
    
    payload = {
        "prompt": prompt,
        "model": model,
        "aspectRatio": "9:16"
    }
    
    # Add reference images for character consistency
    if reference_images:
        import base64
        refs = []
        for img_path in reference_images:
            with open(img_path, "rb") as f:
                refs.append(base64.b64encode(f.read()).decode())
        payload["referenceImages"] = refs
    
    job_id = submit_job("/v1/nano-banana/image", payload)
    result = poll_status(job_id)
    
    download_file(result["imageUrl"], f"output/nano_{job_id}.png")
    return result["imageUrl"]

Example: Create AI Influencer (10-image character sheet)

def create_ai_influencer(description):
    """Generate 10-angle character sheet for AI influencer"""
    
    # Step 1: Generate hero front portrait
    hero_prompt = f"""
{description}
Front-facing portrait, natural expression, golden hour lighting.
Photoreal skin texture, freckles, pores visible.
Soft focus background, kitchen setting.
"""
    
    hero_url = create_nano_banana_image(hero_prompt)
    print(f"Hero portrait: {hero_url}")
    print("Review and approve hero before generating remaining 9 angles.")
    
    # Step 2: Generate 9 additional angles using hero as reference
    angles = [
        "3/4 view left, slight smile",
        "3/4 view right, natural expression",
        "Profile left, looking away",
        "Profile right, looking forward",
        "Close-up, eyes focused on camera",
        "Full body, standing casual pose",
        "Laughing, animated expression",
        "Serious expression, direct gaze",
        "Lifestyle shot, holding coffee mug"
    ]
    
    results = []
    for angle in angles:
        prompt = f"{description}\n{angle}\nMaintain exact character likeness."
        url = create_nano_banana_image(
            prompt,
            reference_images=["output/hero_portrait.png"],
            model="nano-banana"  # Use Pro for tighter identity lock
        )
        results.append(url)
    
    return results

ChatGPT Image 2 (Typography & UI-Heavy Ads)

Best for: Apple Notes lists, fake Slack threads, editorial layouts, comparison tables

def generate_chatgpt_image(prompt, aspect_ratio="1:1"):
    """Generate image with ChatGPT Image 2 (gpt-image-2)"""
    
    payload = {
        "prompt": prompt,
        "model": "gpt-image-2",
        "aspectRatio": aspect_ratio  # "1:1", "4:5", "16:9"
    }
    
    job_id = submit_job("/v1/image/generate", payload)
    result = poll_status(job_id)
    
    download_file(result["imageUrl"], f"output/chatgpt_{job_id}.png")
    return result["imageUrl"]

Static Meta Image Ad Templates (37-Template Library)

The repo includes 37 validated prompt templates for static Meta image ads. Use the specialized skills:

# Apple Notes-style list ad
def generate_apple_notes_ad(product, benefits):
    """Generate Apple Notes-style ad (ChatGPT Image 2)"""
    
    prompt = f"""
iPhone Notes app interface, cream background.
Title: "why i switched to {product}"
Bulleted list:
{chr(10).join(f'• {b}' for b in benefits)}

Footer: handwritten-style signature.
Clean iOS typography, authentic spacing, no visible phone edges.
"""
    
    return generate_chatgpt_image(prompt, aspect_ratio="4:5")

# Photoreal UGC selfie ad
def generate_ugc_selfie_ad(influencer_ref, product_ref):
    """Generate UGC selfie with product (Nano Banana)"""
    
    prompt = """
iPhone selfie, natural bedroom lighting.
[influencer] holding [product], casual smile.
Authentic skin texture, slight motion blur.
Visible pores, flyaway hairs, iPhone camera imperfections.
Product visible and recognizable, natural hand position.
"""
    
    return create_nano_banana_image(
        prompt,
        reference_images=[influencer_ref, product_ref],
        model="nano-banana-2"
    )

Template categories (see shared/skills/image-ad-prompting/library/ for full list):

UI/App Mimicry: Apple Notes, Slack threads, iMessage screenshots, ChatGPT conversations, Google search results
Editorial: Forbes hero, magazine cover, billboard, museum exhibit
Comparison: Feature tables, before/after splits, versus cards
Lifestyle: Sticky-note flatlays, founder letter, dating-app card, weather forecast UI

Multi-Step Animated Ad Pipelines

Pixar-Style 3D Animated Ad

8-beat story arc → ChatGPT Image 2 storyboard → Seedance 2.0 i2v per beat → ffmpeg stitch + captions

def generate_pixar_ad(product, story_arc):
    """
    Generate Pixar-style animated ad
    story_arc: list of 8 beat descriptions
    """
    
    # Step 1: Lock cast sheet (character designs)
    cast_prompt = """
3D Pixar style character sheet.
Anthropomorphized [product mascot], friendly expression.
Multiple angles: front, 3/4, profile, back.
Consistent lighting, white background.
"""
    
    cast_url = generate_chatgpt_image(cast_prompt)
    print(f"Cast sheet: {cast_url}")
    
    # Step 2: Generate storyboard stills (8 beats)
    stills = []
    prev_frame = None
    
    for i, beat in enumerate(story_arc):
        prompt = f"""
3D Pixar animation style frame.
Beat {i+1}: {beat}
Maintain character consistency from cast sheet.
Cinematic lighting, shallow depth of field.
"""
        
        refs = [cast_url]
        if prev_frame:
            refs.append(prev_frame)  # Max 5 references total
        
        # Use ChatGPT Image 2 for sequential stills
        job_id = submit_job("/v1/image/generate", {
            "prompt": prompt,
            "model": "gpt-image-2",
            "referenceImages": refs,
            "aspectRatio": "16:9"
        })
        
        result = poll_status(job_id)
        still_path = f"output/beat_{i+1}.png"
        download_file(result["imageUrl"], still_path)
        
        stills.append(still_path)
        prev_frame = result["imageUrl"]
    
    # Step 3: Animate each still with Seedance 2.0 i2v
    videos = []
    for i, still in enumerate(stills):
        video = animate_still_with_seedance(still, story_arc[i])
        videos.append(video)
    
    # Step 4: Stitch with ffmpeg
    stitch_videos(videos, "output/pixar_ad_final.mp4")
    
    # Step 5: Burn captions (optional)
    # burn_captions("output/pixar_ad_final.mp4", "output/pixar_ad_captioned.mp4")
    
    return "output/pixar_ad_final.mp4"

def animate_still_with_seedance(image_path, motion_description):
    """Animate a still image with Seedance 2.0 i2v"""
    
    import base64
    with open(image_path, "rb") as f:
        img_b64 = base64.b64encode(f.read()).decode()
    
    payload = {
        "startFrame": img_b64,
        "prompt": f"{motion_description}\nMaintain scene composition, add subtle motion.",
        "duration": 3
    }
    
    job_id = submit_job("/v2/videos/generate", payload)
    result = poll_status(job_id)
    
    output_path = f"output/animated_{int(time.time())}.mp4"
    download_file(result["videoUrl"], output_path)
    return output_path

def stitch_videos(video_paths, output_path):
    """Stitch multiple videos with ffmpeg"""
    
    import subprocess
    
    # Create concat file
    with open("concat_list.txt", "w") as f:
        for path in video_paths:
            f.write(f"file '{path}'\n")
    
    subprocess.run([
        "ffmpeg", "-f", "concat", "-safe", "0",
        "-i", "concat_list.txt",
        "-c", "copy", output_path
    ], check=True)

Caption Burn-In Workflow

def burn_captions(video_path, output_path):
    """Transcribe and burn captions onto video"""
    
    import subprocess
    import whisper
    
    # Step 1: Transcribe with Whisper
    model = whisper.load_model("medium.en")
    result = model.transcribe(video_path, word_timestamps=True)
    
    # Step 2: Group word-level timestamps into phrases
    phrases = group_into_phrases(result["segments"])
    
    # Step 3: Generate caption JSON for HyperFrames
    captions_json = {
        "captions": [
            {
                "start": p["start"],
                "end": p["end"],
                "text": p["text"]
            }
            for p in phrases
        ]
    }
    
    with open("captions.json", "w") as f:
        import json
        json.dump(captions_json, f)
    
    # Step 4: Render with HyperFrames
    subprocess.run([
        "npx", "hyperframes",
        "--input", video_path,
        "--captions", "captions.json",
        "--output", output_path,
        "--style", "modern"  # or "classic", "minimal"
    ], check=True)

def group_into_phrases(segments, max_words=5):
    """Group word-level timestamps into reading phrases"""
    
    phrases = []
    current_phrase = {"words": [], "start": 0, "end": 0}
    
    for segment in segments:
        for word_info in segment.get("words", []):
            current_phrase["words"].append(word_info["word"])
            
            if not current_phrase["start"]:
                current_phrase["start"] = word_info["start"]
            
            current_phrase["end"] = word_info["end"]
            
            if len(current_phrase["words"]) >= max_words:
                phrases.append({
                    "text": " ".join(current_phrase["words"]),
                    "start": current_phrase["start"],
                    "end": current_phrase["end"]
                })
                current_phrase = {"words": [], "start": 0, "end": 0}
    
    # Add remaining words
    if current_phrase["words"]:
        phrases.append({
            "text": " ".join(current_phrase["words"]),
            "start": current_phrase["start"],
            "end": current_phrase["end"]
        })
    
    return phrases

Configuration

All configuration is in .env:

ARCADS_API_KEY=your_api_key_here

# Optional: default models
DEFAULT_VIDEO_MODEL=seedance-2
DEFAULT_IMAGE_MODEL=nano-banana-2

# Optional: output directories
OUTPUT_DIR=output
REFERENCES_DIR=references

# Optional: API timeouts
JOB_TIMEOUT=600
POLL_INTERVAL=10

Common Workflows

Full UGC Video Workflow (Still → Video)

def create_ugc_video_from_scratch(influencer_desc, product_ref, script):
    """Complete UGC workflow: character → still → video"""
    
    # Step 1: Create AI influencer if needed
    influencer_refs = create_ai_influencer(influencer_desc)
    hero_ref = influencer_refs[0]
    
    # Step 2: Generate UGC still with product
    still_prompt = f"""
{influencer_desc}
iPhone selfie, bedroom setting, natural lighting.
Holding {product_ref}, casual smile, looking at camera.
Authentic skin texture, slight motion blur.
"""
    
    still_url = create_nano_banana_image(
        still_prompt,
        reference_images=[hero_ref, product_ref]
    )
    
    download_file(still_url, "temp_still.png")
    
    # Step 3: Animate with Veo 3.1 + dialogue
    video_url = animate_with_veo("temp_still.png", script, duration=12)
    
    return video_url

Batch Video Generation

def batch_generate_videos(prompts, model="seedance-2"):
    """Submit multiple video jobs in parallel"""
    
    job_ids = []
    
    # Submit all jobs
    for prompt in prompts:
        payload = {
            "prompt": prompt,
            "duration": 10,
            "aspectRatio": "9:16"
        }
        job_id = submit_job("/v2/videos/generate", payload)
        job_ids.append(job_id)
        print(f"Submitted: {job_id}")
    
    # Poll all jobs
    results = []
    for job_id in job_ids:
        try:
            result = poll_status(job_id, timeout=900)
            video_path = f"output/batch_{job_id}.mp4"
            download_file(result["videoUrl"], video_path)
            results.append(video_path)
        except Exception as e:
            print(f"Job {job_id} failed: {e}")
    
    return results

Cost Estimation

def estimate_cost(model, duration=10):
    """Estimate generation cost before submitting"""
    
    costs = {
        "seedance-2": 0.10 * (duration / 10),  # $0.10 per 10s
        "veo-3-1": 0.15 * (duration / 10),
        "sora-2": 0.20 * (duration / 10),
        "kling-3": 0.08 * (duration / 10),
        "nano-banana-2": 0.05,  # per image
        "nano-banana": 0.08,  # Pro
        "gpt-image-2": 0.04
    }
    
    return costs.get(model, 0.10)

# Confirm before generating
cost = estimate_cost("seedance-2", duration=12)
print(f"Estimated cost: ${cost:.2f}")
response = input("Proceed? (y/n): ")
if response.lower() == "y":
    generate_seedance_video(prompt, duration=12)

Troubleshooting

API Key Issues

def verify_api_connection():
    """Test API key validity"""
    
    try:
        response = requests.get(
            f"{BASE_URL}/v1/account",
            headers=headers
        )
        response.raise_for_status()
        print("✓ API connection successful")
        print(f"Account: {response.json()['email']}")
        return True
    except requests.exceptions.HTTPError as e:
        if e.response.status_code == 401:
            print("✗ Invalid API key")
            print("Get your key: https://app.arcads.ai/settings/api")
        else:
            print(f"✗ API error: {e}")
        return False

Job Timeout Issues

If jobs are timing out (Veo/Sora can take 15+ minutes):

# Increase timeout for slower models
result = poll_status(job_id, timeout=1800, interval=30)  # 30min, check every 30s

Character Consistency Issues

For better character lock across multiple images:

# Use Nano Banana Pro instead of default
create_nano_banana_image(
    prompt,
    reference_images=[hero_ref],
    model="nano-banana"  # Pro model (Gemini 3 Pro Image)
)

# Provide 3-5 reference images from different angles
refs = [
    "references/influencers/sofia_front.png",
    "references/influencers/sofia_3_4.png",
    "references/influencers/sofia_profile.png"
]

Video Quality Issues

If Seedance videos lack motion or feel static:

# Add explicit motion cues to prompt
prompt = """
[SCENE] Woman in kitchen, dynamic camera movement
[MOTION] Head turns toward camera, hand gestures, product raise
[ACTION] Walks forward 2 steps, smiles broadly
[ENERGY] Animated, enthusiastic, constant subtle movement
"""

ffmpeg Not Found

# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt install ffmpeg

# Verify installation
ffmpeg -version

Advanced: Publishing to Meta Ads

The repo includes a meta-ad-builder skill for publishing generated images as paused Meta ads:

# Install dependencies first
# pip install -r shared/skills/meta-ad-builder/scripts/requirements.txt

def publish_to_meta(image_path, ad_account_id, campaign_name):
    """Publish generated image as paused Meta ad"""
    
    import subprocess
    
    # Set required env vars
    os.environ["META_ACCESS_TOKEN"] = os.getenv("META_ACCESS_TOKEN")
    os.environ["AD_ACCOUNT_ID"] = ad_account_id
    
    # Run meta-ad-builder script
    subprocess.run([
        "python",
        "shared/skills/meta-ad-builder/scripts/create_ad.py",
        "--image", image_path,
        "--campaign", campaign_name,
        "--status", "PAUSED"
    ], check=True)

Requires META_ACCESS_TOKEN and AD_ACCOUNT_ID in .env.

File Organization

The agent auto-organizes outputs:

project/
├── output/                    # Generated assets
│   ├── seedance_*.mp4
│   ├── nano_*.png
│   └── pixar_ad_final.mp4
├── references/                # Reference libraries
│   ├── influencers/          # AI character sheets
│   ├── products/             # Product photos
│   └── aesthetics/           # Style references
├── .env                       # API keys (gitignored)
└── MASTER_CONTEXT.md         # Project workspace file

Prompt Engineering Tips

Seedance 2.0 UGC Formula

Use the 9-layer structure for authentic UGC:

[PERSON] demographics + appearance
[OPENER] eye contact + greeting
[HOOK] attention-grabbing statement
[PROBLEM] relatable pain point
[SOLUTION] product intro
[DEMO] product in action
[BENEFIT] key value prop
[SOCIAL_PROOF] testimonial feel
[CTA] natural call to action
[STYLE] iPhone aesthetic + lighting

Nano Banana Character Consistency

For AI influencers that look the same across 100+ images:

Generate hero portrait with detailed description
Use hero as referenceImages[0] for all subsequent generations
Use Nano Banana Pro (model="nano-banana") for tighter lock
Include "maintain exact character likeness" in every prompt
Lock lighting/background when possible (e.g., "golden hour kitchen" across all shots)

Veo 3.1 Dialogue Best Practices

Always include dialogue field (mandatory for talking videos)
Keep dialogue under 20 words for 8s clips (~2.5 words/sec)
Prompt for "natural head movement, slight pauses, eye contact breaks"
Starting frame should show person's face clearly (not profile/back)

API Endpoints Reference

# Video generation
"/v2/videos/generate"           # Seedance 2.0, Sora 2, Grok Video
"/v1/veo3-1/video"              # Veo 3.1 with startFrame + dialogue
"/v1/sora2/video"               # Sora 2 text-to-video
"/v1/sora2/remix/video"         # Sora 2 remix
"/v1/b-roll"                    # Kling 3.0 b-roll
"/v1/scene"                     # Kling 3.0 scene
"/v1/omnihuman"                 # OmniHuman avatar
"/v1/audio-driven"              # Audio-driven lip-sync

# Image generation
"/v1/nano-banana/image"         # Nano Banana 2/Pro/Edit
"/v1/image/generate"            # ChatGPT Image 2

# Job management
"/v1/jobs/{jobId}"              # Poll job status
"/v1/account"                   # Verify API key

Cost Management

Always confirm costs before batch operations:

def confirm_batch_cost(num_videos, model="seedance-2", duration=10):
    """Confirm cost before batch generation

arcads-ai-video-agent

More from this repository

Arcads AI Video Agent

Prerequisites

Installation

Core API Patterns

Standard Generation Flow

Video Generation

Seedance 2.0 (Flagship Model)

Veo 3.1 (Image-to-Video with Dialogue)

Sora 2 (Text-to-Video, Longer Durations)

Kling 3.0 (B-Roll & Scenes)

Image Generation

Nano Banana (Character Creation & Product Stills)

ChatGPT Image 2 (Typography & UI-Heavy Ads)

Static Meta Image Ad Templates (37-Template Library)

Multi-Step Animated Ad Pipelines

Pixar-Style 3D Animated Ad

Caption Burn-In Workflow

Configuration

Common Workflows

Full UGC Video Workflow (Still → Video)

Batch Video Generation

Cost Estimation

Troubleshooting

API Key Issues

Job Timeout Issues

Character Consistency Issues

Video Quality Issues

ffmpeg Not Found

Advanced: Publishing to Meta Ads

File Organization

Prompt Engineering Tips

Seedance 2.0 UGC Formula

Nano Banana Character Consistency

Veo 3.1 Dialogue Best Practices

API Endpoints Reference

Cost Management

Arcads AI Video Agent

Prerequisites

Installation

Core API Patterns

Standard Generation Flow

Video Generation

Seedance 2.0 (Flagship Model)

Veo 3.1 (Image-to-Video with Dialogue)

Sora 2 (Text-to-Video, Longer Durations)

Kling 3.0 (B-Roll & Scenes)

Image Generation

Nano Banana (Character Creation & Product Stills)

ChatGPT Image 2 (Typography & UI-Heavy Ads)

Static Meta Image Ad Templates (37-Template Library)

Multi-Step Animated Ad Pipelines

Pixar-Style 3D Animated Ad

Caption Burn-In Workflow

Configuration

Common Workflows

Full UGC Video Workflow (Still → Video)

Batch Video Generation

Cost Estimation

Troubleshooting

API Key Issues

Job Timeout Issues

Character Consistency Issues

Video Quality Issues

ffmpeg Not Found

Advanced: Publishing to Meta Ads

File Organization

Prompt Engineering Tips

Seedance 2.0 UGC Formula

Nano Banana Character Consistency

Veo 3.1 Dialogue Best Practices

API Endpoints Reference

Cost Management

More from this repository