# Arcads AI Video Generator > Create AI marketing videos and static Meta image ads using the Arcads API with Seedance 2.0, Sora 2, Veo 3.1, Kling, Nano Banana, ChatGPT Image, and 37 static ad templates - Skill: `aradotso/arcads-ai-video-generator` (Agent Skill) - Install (CLI): `npx skillmds@latest add aradotso/arcads-ai-video-generator` - Raw SKILL.md: https://api.skillmd.com/api/skills/aradotso/arcads-ai-video-generator/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: aradotso (https://skillmd.com/u/aradotso) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/aradotso/arcads-ai-video-generator --- # Arcads AI Video Generator > Skill by [ara.so](https://ara.so) — Claude Code Skills collection. Create AI marketing videos and images using the Arcads API. 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**, **Audio-driven** models, plus a 37-template static Meta image-ad library and multi-step pipelines for Pixar-style and claymation animated ads. ## Installation ```bash git clone https://github.com/krusemediallc/arcads-claude-code.git cd arcads-claude-code ./scripts/setup.sh ``` The setup script will: - Prompt for your Arcads API key (get it at [app.arcads.ai/settings/api](https://app.arcads.ai/settings/api)) - Save credentials to `.env` - Verify API connection - Create `MASTER_CONTEXT.md` workspace file ### Prerequisites | Tool | Required for | Install | |---|---|---| | Python 3.10+ | Core API operations | `brew install python@3.12` | | `ffmpeg` | Video stitching, chroma-key | `brew install ffmpeg` | | `jq` | JSON parsing in bash scripts | `brew install jq` | | Node.js | Caption burn-in (hyperframes) | `brew install node` | | `whisper` | Caption transcription | `pip install openai-whisper` | ### Environment Setup Create `.env` in the project root: ```bash ARCADS_API_KEY=your_api_key_here ``` ## Core API Patterns ### Video Generation (Seedance 2.0) **Endpoint:** `POST https://api.arcads.ai/v1/seedance2/video` ```python import requests import os import time API_KEY = os.getenv('ARCADS_API_KEY') BASE_URL = 'https://api.arcads.ai' def generate_seedance_video(prompt, duration=12): """Generate a Seedance 2.0 video.""" headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } payload = { 'prompt': prompt, 'duration': duration, 'shotStyle': 'ugc-selfie' # or 'premium-reveal', 'product-hero', etc. } # Start generation response = requests.post( f'{BASE_URL}/v1/seedance2/video', headers=headers, json=payload ) job = response.json() job_id = job['id'] # Poll until complete while True: status_response = requests.get( f'{BASE_URL}/v1/jobs/{job_id}', headers=headers ) status = status_response.json() if status['status'] == 'completed': return status['videoUrl'] elif status['status'] == 'failed': raise Exception(f"Generation failed: {status.get('error')}") time.sleep(5) # Example: UGC product review video_url = generate_seedance_video( prompt=""" [SCENE: NATURAL KITCHEN LIGHTING] Medium shot, slight handheld motion. Woman, early 30s, messy bun, casual tank top. Holding [PRODUCT] at chest level, genuine smile. [DIALOGUE] "Okay so I used to buy [COMPETITOR] every month but then I found THIS—" *lifts product slightly, direct eye contact* "—and honestly? I'm never going back." [VISUAL BEATS] 0-3s: Establishing shot, product reveal 3-7s: Close on face during key claim 7-12s: Pull back, casual product demo gesture """, duration=12 ) print(f"Video ready: {video_url}") ``` ### Seedance 2.0 Prompt Formulas Five battle-tested formulas ship with the skill: #### 1. UGC Selfie-Style Review ``` [SCENE: NATURAL KITCHEN LIGHTING] Medium shot, slight handheld motion. Woman, early 30s, messy bun, casual tank top. Holding [PRODUCT] at chest level, genuine smile. [DIALOGUE] "Okay so I used to buy [COMPETITOR] every month..." [VISUAL BEATS] 0-3s: Establishing shot 3-7s: Close on face 7-12s: Product demo gesture ``` #### 2. Premium Product Reveal (No Person) ``` [SCENE: DARK VOID] No human. Product only. Black background, dramatic side lighting. [TEXT OVERLAYS] Beat 1 (0-3s): "Most [CATEGORY] products..." Beat 2 (3-6s): "[PRODUCT] is different." Beat 3 (6-9s): Hero rotation + key feature callout [CAMERA] Slow push-in, 360° rotation at beat 3 ``` #### 3. Product Hero with Elements ``` [SCENE: ELEMENTAL SHOWCASE] Product suspended mid-frame. Water splash from below, mist rising. Slow rotation (15°/sec). [VISUAL FX] 0-2s: Water splash entry 2-5s: Mist buildup 5-8s: Light rays piercing mist 8-12s: Hero rotation + feature close-up ``` ### Image Generation (Nano Banana) **Endpoint:** `POST https://api.arcads.ai/v1/nano-banana/image` ```python import base64 def generate_nano_banana_image(prompt, reference_image_path=None, model='nano-banana-2'): """Generate a Nano Banana image with optional reference.""" headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } payload = { 'prompt': prompt, 'model': model, # 'nano-banana-2' or 'nano-banana' (Pro) or 'nano-banana-edit' 'aspectRatio': '9:16' } # Add reference image if provided if reference_image_path: with open(reference_image_path, 'rb') as f: img_base64 = base64.b64encode(f.read()).decode('utf-8') payload['refImageAsBase64'] = img_base64 response = requests.post( f'{BASE_URL}/v1/nano-banana/image', headers=headers, json=payload ) job = response.json() job_id = job['id'] # Poll for completion while True: status_response = requests.get( f'{BASE_URL}/v1/jobs/{job_id}', headers=headers ) status = status_response.json() if status['status'] == 'completed': return status['imageUrl'] elif status['status'] == 'failed': raise Exception(f"Generation failed: {status.get('error')}") time.sleep(3) # Example: UGC selfie with product image_url = generate_nano_banana_image( prompt=""" iPhone selfie camera, natural bedroom lighting. Woman, 22, freckles, golden hour glow. Holding [PRODUCT] at chest level, genuine smile. Messy hair, no makeup, casual tank top. Slight camera shake, realistic skin texture. Background: unmade bed, string lights, plants. """, reference_image_path='references/influencers/sofia_hero.jpg' ) ``` ### Veo 3.1 (Start-Frame Animation) **Endpoint:** `POST https://api.arcads.ai/v1/veo3/video` ```python def animate_with_veo(start_frame_path, prompt, duration=8): """Animate a still image with Veo 3.1.""" headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } # Load start frame with open(start_frame_path, 'rb') as f: img_base64 = base64.b64encode(f.read()).decode('utf-8') payload = { 'prompt': prompt, 'startFrame': img_base64, 'duration': duration, 'dialogue': 'I switched to this product and never looked back' } response = requests.post( f'{BASE_URL}/v1/veo3/video', headers=headers, json=payload ) job = response.json() return poll_job(job['id']) def poll_job(job_id): """Generic job polling.""" headers = {'Authorization': f'Bearer {API_KEY}'} while True: response = requests.get( f'{BASE_URL}/v1/jobs/{job_id}', headers=headers ) status = response.json() if status['status'] == 'completed': return status.get('videoUrl') or status.get('imageUrl') elif status['status'] == 'failed': raise Exception(f"Job failed: {status.get('error')}") time.sleep(5) ``` ### Sora 2 Video Generation **Endpoint:** `POST https://api.arcads.ai/v1/sora2/video` ```python def generate_sora_video(prompt, duration=16, reference_image_path=None): """Generate a Sora 2 video (up to 20s).""" headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } payload = { 'prompt': prompt, 'duration': duration } if reference_image_path: with open(reference_image_path, 'rb') as f: img_base64 = base64.b64encode(f.read()).decode('utf-8') payload['styleReference'] = img_base64 response = requests.post( f'{BASE_URL}/v1/sora2/video', headers=headers, json=payload ) return poll_job(response.json()['id']) # Example: Longer narrative scene video_url = generate_sora_video( prompt=""" Cozy coffee shop interior, warm afternoon light. Camera tracks across table as woman opens laptop. Smooth dolly shot, shallow depth of field. Steam rising from coffee mug in foreground. Natural color grading, 24fps cinematic feel. """, duration=16 ) ``` ### B-Roll / Scene Generation (Kling 3.0) **Endpoint:** `POST https://api.arcads.ai/v1/b-roll` or `POST https://api.arcads.ai/v1/scene` ```python def generate_broll(prompt, duration=5): """Generate b-roll footage with Kling 3.0.""" headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } payload = { 'prompt': prompt, 'duration': duration } response = requests.post( f'{BASE_URL}/v1/b-roll', headers=headers, json=payload ) return poll_job(response.json()['id']) # Example: Product environment b-roll broll_url = generate_broll( prompt=""" Close-up of coffee beans being poured into grinder. Slow motion, 120fps. Natural light from window, wooden countertop. Shallow focus on falling beans. """ ) ``` ## Static Meta Image Ad Library The repo includes **37 validated prompt templates** for static Meta image ads. Two primary generators: ### ChatGPT Image 2 (Typography/UI-Heavy) **Best for:** Apple Notes lists, editorial layouts, comparison tables, UI mockups, text-heavy designs ```python def generate_chatgpt_image_ad(template_name, product_details): """Generate static ad using ChatGPT Image 2.""" # Load template from library template_path = f'shared/skills/image-ad-prompting/library/{template_name}.md' with open(template_path, 'r') as f: template = f.read() # Fill template with product details prompt = template.format(**product_details) headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } payload = { 'prompt': prompt, 'model': 'gpt-image-2', 'aspectRatio': '4:5' # Standard Meta image ad ratio } response = requests.post( f'{BASE_URL}/v1/chatgpt-image/generate', headers=headers, json=payload ) return poll_job(response.json()['id']) # Example: Apple Notes style ad image_url = generate_chatgpt_image_ad( template_name='apple-notes-list', product_details={ 'product_name': 'FocusFlow', 'category': 'productivity app', 'benefits': [ 'Blocks distractions automatically', 'AI suggests optimal focus times', 'Syncs across all devices' ], 'cta': 'Download free today' } ) ``` ### Nano Banana (Photoreal/Lifestyle) **Best for:** Lifestyle product shots, before/after, founder letters with portraits, real-world settings ```python def generate_nano_banana_ad(template_name, product_details, reference_images=None): """Generate static ad using Nano Banana.""" template_path = f'shared/skills/image-ad-prompting/library/{template_name}.md' with open(template_path, 'r') as f: template = f.read() prompt = template.format(**product_details) headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } payload = { 'prompt': prompt, 'model': 'nano-banana-2', 'aspectRatio': '4:5' } # Add reference images if provided if reference_images: ref_images_b64 = [] for img_path in reference_images: with open(img_path, 'rb') as f: ref_images_b64.append(base64.b64encode(f.read()).decode('utf-8')) payload['referenceImages'] = ref_images_b64 response = requests.post( f'{BASE_URL}/v1/nano-banana/image', headers=headers, json=payload ) return poll_job(response.json()['id']) ``` ### Available Templates (37 total) Key templates in the library: - `apple-notes-list` — Simple checklist aesthetic - `forbes-editorial` — Magazine hero layout - `comparison-table` — Side-by-side feature grid - `fake-google-search` — Search results UI mockup - `sticky-note-flatlay` — Scattered notes on desk - `imessage-screenshot` — Text conversation mockup - `chatgpt-conversation` — AI chat interface - `weather-forecast-ui` — Weather app parody - `dating-app-card` — Swipe-style card layout - `billboard-mockup` — Outdoor advertising placement - `museum-exhibit` — Art gallery presentation - `scratch-off-ticket` — Lottery/game aesthetic See `shared/skills/image-ad-prompting/OVERVIEW.md` for full template list and compatibility matrix. ## Multi-Step Pipelines ### Pixar-Style 3D Animated Ad Creates an 8-beat story arc with anthropomorphized characters, stitched into a final video with captions. **Workflow:** 1. Define character sheet (mascot design) 2. Generate storyboard stills (ChatGPT Image 2) 3. Animate each beat (Seedance 2.0 i2v) 4. Stitch segments (ffmpeg) 5. Burn captions (HyperFrames) ```bash # Run the full pipeline ./shared/skills/pixar-style-ad/pipeline.sh \ --product "EcoBottle" \ --duration 60 \ --output output/pixar-ad.mp4 ``` **Python API approach:** ```python def create_pixar_ad(product_name, story_beats): """Generate a Pixar-style animated ad.""" # Step 1: Generate character design character_prompt = f""" 3D Pixar style mascot for {product_name}. Anthropomorphized product with expressive face. Big eyes, friendly smile, rounded shapes. Soft lighting, vibrant colors, clean geometry. T-pose reference, white background. """ character_img = generate_chatgpt_image(character_prompt) # Step 2: Generate storyboard frames storyboard_frames = [] prev_frame = character_img for beat in story_beats: frame_prompt = f""" {beat['scene_description']} Character from reference image. Pixar 3D render style, consistent lighting. """ frame_url = generate_chatgpt_image( prompt=frame_prompt, reference_images=[prev_frame] ) storyboard_frames.append(frame_url) prev_frame = frame_url # Step 3: Animate each frame with Seedance 2.0 video_segments = [] for i, frame in enumerate(storyboard_frames): video_url = animate_with_seedance_i2v( start_frame=frame, duration=story_beats[i]['duration'], motion_prompt=story_beats[i]['motion'] ) video_segments.append(video_url) # Step 4: Stitch (requires ffmpeg) final_video = stitch_videos(video_segments) return final_video def animate_with_seedance_i2v(start_frame, duration, motion_prompt): """Animate a still frame with Seedance 2.0 image-to-video.""" headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } # Download and encode start frame import requests as req img_data = req.get(start_frame).content img_base64 = base64.b64encode(img_data).decode('utf-8') payload = { 'startFrame': img_base64, 'motionPrompt': motion_prompt, 'duration': duration } response = requests.post( f'{BASE_URL}/v1/seedance2/video', headers=headers, json=payload ) return poll_job(response.json()['id']) # Example usage pixar_ad = create_pixar_ad( product_name='EcoBottle', story_beats=[ { 'scene_description': 'Mascot looking sad at pile of plastic waste', 'duration': 6, 'motion': 'Subtle head turn, eyes downcast' }, { 'scene_description': 'Mascot has idea, lightbulb appears', 'duration': 4, 'motion': 'Excited jump, arms raised' }, { 'scene_description': 'Mascot presents EcoBottle to camera', 'duration': 8, 'motion': 'Slow rotation of bottle, proud smile' } # ... 5 more beats ] ) ``` ### AI Influencer Creation (10-Image Character Sheet) Generates a consistent AI influencer with multiple angles for reuse. ```python def create_ai_influencer(description): """Create a 10-image character sheet for an AI influencer.""" # Step 1: Generate hero front portrait hero_prompt = f""" Front-facing portrait, iPhone selfie camera. {description} Natural lighting, genuine smile. 9:16 aspect ratio, eye-level camera. Realistic skin texture, no AI smoothing. """ hero_url = generate_nano_banana_image( prompt=hero_prompt, model='nano-banana' # Use Pro for tighter identity lock ) print(f"Hero generated: {hero_url}") print("Review and approve before generating additional angles...") # Wait for user approval (in agent context) input("Press Enter when approved...") # Step 2: Generate 9 additional angles angles = [ ('3/4 view, slight smile, looking slightly left', 3), ('Profile view, side lighting', 3), ('Close-up of face, neutral expression', 2), ('Full body shot, standing pose', 1), ('Laughing expression, candid moment', 2), ('Serious expression, professional', 2) # ... 3 more angles ] character_sheet = [hero_url] for angle_desc, count in angles: for _ in range(count): angle_prompt = f""" {description} {angle_desc} Match character from reference image exactly. Same lighting style, same person. """ img_url = generate_nano_banana_image( prompt=angle_prompt, reference_image_path=download_temp(hero_url), model='nano-banana' # Pro for consistency ) character_sheet.append(img_url) # Save to references folder save_character_sheet('sofia', character_sheet) return character_sheet # Example influencer = create_ai_influencer( description=""" Woman, 24 years old, warm smile. Light freckles, wavy brown hair. College student aesthetic. Natural makeup, approachable energy. """ ) ``` ### Caption Burn-In Workflow Add captions to any finished video using Whisper + HyperFrames. ```bash # Requires: pip install openai-whisper, npm install -g hyperframes ./shared/skills/caption-video/burn_captions.sh \ --input output/video.mp4 \ --output output/video_captioned.mp4 \ --style "viral" ``` **Python implementation:** ```python import whisper import subprocess import json def burn_captions(video_path, output_path, style='viral'): """Add captions to video using Whisper + HyperFrames.""" # Step 1: Transcribe with Whisper model = whisper.load_model("medium.en") result = model.transcribe(video_path, word_timestamps=True) # Step 2: Group words into reading phrases phrases = group_into_phrases(result['segments']) # Step 3: Generate HyperFrames caption JSON caption_json = generate_hyperframes_config(phrases, style) with open('captions_config.json', 'w') as f: json.dump(caption_json, f) # Step 4: Render with HyperFrames subprocess.run([ 'npx', 'hyperframes', '--input', video_path, '--config', 'captions_config.json', '--output', output_path ]) return output_path def group_into_phrases(segments, max_words=4): """Group word-level timestamps into reading phrases.""" phrases = [] current_phrase = [] for segment in segments: for word in segment.get('words', []): current_phrase.append(word) if len(current_phrase) >= max_words or word['word'].endswith(('.', '!', '?')): phrases.append({ 'text': ' '.join([w['word'] for w in current_phrase]), 'start': current_phrase[0]['start'], 'end': current_phrase[-1]['end'] }) current_phrase = [] return phrases def generate_hyperframes_config(phrases, style): """Generate HyperFrames caption configuration.""" styles = { 'viral': { 'font': 'Montserrat-ExtraBold', 'fontSize': 72, 'color': '#FFFFFF', 'stroke': '#000000', 'strokeWidth': 6, 'position': 'center' }, 'minimal': { 'font': 'Helvetica-Neue', 'fontSize': 48, 'color': '#FFFFFF', 'stroke': 'none', 'position': 'bottom' } } return { 'captions': phrases, 'style': styles.get(style, styles['viral']) } ``` ## Configuration ### API Base URL Default: `https://api.arcads.ai` Override via environment variable: ```bash export ARCADS_API_BASE_URL=https://custom-endpoint.com ``` ### Model Selection ```python # Video models SEEDANCE_2 = 'seedance-2' SORA_2 = 'sora-2' VEO_3_1 = 'veo-3.1' KLING_3 = 'kling-3.0' GROK_VIDEO = 'grok-video' # Image models NANO_BANANA_2 = 'nano-banana-2' # Standard NANO_BANANA_PRO = 'nano-banana' # Gemini 3 Pro (higher fidelity) NANO_BANANA_EDIT = 'nano-banana-edit' # Inpainting CHATGPT_IMAGE_2 = 'gpt-image-2' ``` ### Aspect Ratios ```python ASPECT_RATIOS = { 'vertical': '9:16', # Stories, Reels 'square': '1:1', # Feed posts 'meta-image': '4:5', # Meta image ads 'landscape': '16:9' # YouTube, horizontal } ``` ### Duration Limits ```python DURATION_LIMITS = { 'seedance-2': (4, 15), 'sora-2': (1, 20), 'veo-3.1': (4, 15), 'kling-3.0': (1, 10), 'grok-video': (1, 10) } ``` ## Common Patterns ### Pattern: UGC Workflow (Still → Video) ```python def ugc_workflow(influencer_ref, product_ref, script): """Complete UGC workflow: still generation → video animation.""" # Step 1: Generate UGC still still_prompt = f""" iPhone selfie camera, natural bedroom lighting. {influencer_ref['description']} Holding product at chest level, genuine smile. Messy hair, casual tank top. Background: unmade bed, string lights. """ still_url = generate_nano_banana_image( prompt=still_prompt, reference_image_path=influencer_ref['hero_path'] ) print(f"Still generated: {still_url}") input("Review and approve...") # Step 2: Animate with Veo 3.1 video_url = animate_with_veo( start_frame_path=download_temp(still_url), prompt=f""" Natural head and hand movement. Maintains eye contact with camera. Slight smile, authentic energy. Product stays in frame throughout. """, duration=12 ) return video_url ``` ### Pattern: Batch Generation ```python def batch_generate_variations(base_prompt, variations, model='nano-banana-2'): """Generate multiple variations in parallel.""" import concurrent.futures def generate_variation(variation): full_prompt = f"{base_prompt}\n\nVariation: {variation}" return generate_nano_banana_image(full_prompt, model=model) with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor: urls = list(executor.map(generate_variation, variations)) return urls # Example: Generate 6 thumbnail variations thumbnails = batch_generate_variations( base_prompt=""" YouTube thumbnail, 16:9 aspect ratio. Man, 30s, excited expression, peace sign. Product held in other hand. Bold text overlay: "THIS CHANGED EVERYTHING" High contrast, vibrant colors. """, variations=[ 'Blue background, indoor studio lighting', 'Yellow background, warm tone', 'Outdoor natural light, green background', 'Split-screen comparison layout', 'Close-up face, product in foreground', 'Full body shot, product placement emphasized' ] ) ``` ### Pattern: Cost Estimation ```python COST_TABLE = { 'seedance-2': 0.25, # per second 'sora-2': 0.30, 'veo-3.1': 0.20, 'kling-3.0': 0.15, 'nano-banana-2': 0.08, # per image 'nano-banana': 0.12, # Pro 'gpt-image-2': 0.10 } def estimate_cost(model, duration=None, image_count=None): """Estimate generation cost.""" unit_cost = COST_TABLE.get(model, 0) if duration: return unit_cost * duration elif image_count: return unit_cost * image_count return unit_cost # Example cost = estimate_cost('seedance-2', duration=12) print(f"Estimated cost: ${cost:.2f}") user_input = input(f"Proceed with generation? (${cost:.2f}) [y/N]: ")