---

name: ai-video-generator
description: >-
Generate short-form videos with AI — script writing, text-to-speech narration,
stock footage selection, subtitle generation, and video assembly. Use when:
creating TikTok/YouTube Shorts/Reels content, automating video production,
building content pipelines.
license: Apache-2.0
compatibility: "Python 3.10+, FFmpeg"
metadata:
author: terminal-skills
version: "1.0.0"
category: content
tags: [video-generation, tiktok, youtube-shorts, content-creation, ai-video]
use-cases:
- "Generate 50 short-form videos per day for TikTok/YouTube Shorts"
- "Build an automated content pipeline: topic → script → voice → video"
- "Create educational or explainer videos with AI narration"
agents: [claude-code, openai-codex, gemini-cli, cursor]

AI Video Generator — Short-Form Content Pipeline

Overview

Automate creation of short-form videos (TikTok, YouTube Shorts, Instagram Reels) using AI for every step: topic research, script writing, text-to-speech narration, stock footage matching, subtitle generation, and final assembly. Inspired by MoneyPrinterTurbo (53k+ stars).

Instructions

Step 1: Set Up the Environment

pip install anthropic openai requests moviepy pydub whisperx srt
sudo apt install ffmpeg  # Linux — or: brew install ffmpeg (macOS)

API keys needed: Anthropic or OpenAI (scripts), ElevenLabs or OpenAI TTS (voice), Pexels (free stock footage).

Step 2: AI Script Writing

import anthropic

def generate_script(topic, duration_seconds=45):
    """Generate a video script optimized for short-form content."""
    client = anthropic.Anthropic()
    prompt = f"""Write a {duration_seconds}-second video script about: {topic}
    Format:
    HOOK (first 3 seconds): A shocking statement or question that stops scrolling
    BODY (main content): 3-5 punchy facts or points, each 1-2 sentences
    CTA (last 5 seconds): Call to action — follow, like, comment
    Rules:
    - Conversational, no complex sentences
    - Each sentence on its own line
    - ~{duration_seconds * 2.5:.0f} words ({duration_seconds}s at 150wpm)
    - Use power words: secret, shocking, nobody tells you, actually
    - No emojis or hashtags — this is a voiceover script
    """
    response = client.messages.create(
        model="claude-sonnet-4-20250514", max_tokens=500,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text

Step 3: Text-to-Speech Narration

import requests, os

def generate_voice_elevenlabs(text, output_path='narration.mp3'):
    """Generate voiceover using ElevenLabs."""
    url = "https://api.elevenlabs.io/v1/text-to-speech/21m00Tcm4TlvDq8ikWAM"
    headers = {"xi-api-key": os.environ["ELEVENLABS_API_KEY"], "Content-Type": "application/json"}
    data = {"text": text, "model_id": "eleven_turbo_v2_5",
            "voice_settings": {"stability": 0.5, "similarity_boost": 0.75}}
    response = requests.post(url, json=data, headers=headers)
    with open(output_path, 'wb') as f:
        f.write(response.content)
    return output_path

def generate_voice_openai(text, output_path='narration.mp3'):
    """Generate voiceover using OpenAI TTS (cheaper alternative)."""
    from openai import OpenAI
    client = OpenAI()
    response = client.audio.speech.create(model="tts-1-hd", voice="onyx", input=text)
    response.stream_to_file(output_path)
    return output_path

Step 4: Stock Footage Selection

def search_pexels_videos(query, count=5):
    """Search Pexels for portrait-oriented stock video clips."""
    url = "https://api.pexels.com/videos/search"
    headers = {"Authorization": os.environ["PEXELS_API_KEY"]}
    params = {"query": query, "per_page": count, "orientation": "portrait", "size": "medium"}
    response = requests.get(url, headers=headers, params=params)
    videos = response.json().get('videos', [])
    results = []
    for v in videos:
        files = sorted(v['video_files'], key=lambda x: x.get('height', 0), reverse=True)
        hd = next((f for f in files if f.get('height', 0) >= 720), files[0])
        results.append({'id': v['id'], 'url': hd['link'], 'duration': v['duration']})
    return results

Step 5: Subtitle Generation

def generate_subtitles(audio_path, output_srt='subtitles.srt'):
    """Generate word-level subtitles using WhisperX."""
    import whisperx, srt
    from datetime import timedelta
    model = whisperx.load_model("base", device="cpu")
    audio = whisperx.load_audio(audio_path)
    result = model.transcribe(audio)
    align_model, metadata = whisperx.load_align_model(language_code="en")
    aligned = whisperx.align(result["segments"], align_model, metadata, audio)
    subs = []
    words = [w for seg in aligned["segments"] for w in seg.get("words", [])]
    for i in range(0, len(words), 4):
        group = words[i:i + 4]
        if not group: continue
        start = timedelta(seconds=group[0].get('start', 0))
        end = timedelta(seconds=group[-1].get('end', 0))
        text = ' '.join(w['word'] for w in group)
        subs.append(srt.Subtitle(index=len(subs)+1, start=start, end=end, content=text))
    with open(output_srt, 'w') as f:
        f.write(srt.compose(subs))
    return output_srt

Step 6: Video Assembly with FFmpeg

import subprocess

def assemble_video(clips, narration, subtitles, output='final.mp4'):
    """Assemble final video: concatenate clips, add narration and subtitles."""
    concat_list = 'concat_list.txt'
    with open(concat_list, 'w') as f:
        for clip in clips:
            f.write(f"file '{clip}'\n")
    subprocess.run([
        'ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', concat_list,
        '-vf', 'scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920',
        '-c:v', 'libx264', '-preset', 'fast', '-an', 'temp_video.mp4'
    ], check=True)
    subtitle_filter = (f"subtitles={subtitles}:force_style='"
        "FontName=Arial,FontSize=18,PrimaryColour=&H00FFFFFF,"
        "OutlineColour=&H00000000,Outline=2,Bold=1,Alignment=2'")
    subprocess.run([
        'ffmpeg', '-y', '-i', 'temp_video.mp4', '-i', narration,
        '-vf', subtitle_filter, '-c:v', 'libx264', '-c:a', 'aac',
        '-shortest', output
    ], check=True)
    return output

Step 7: Full Pipeline

def generate_video(topic, output_dir='./output'):
    """Complete pipeline: topic -> finished video."""
    import os
    os.makedirs(output_dir, exist_ok=True)
    script = generate_script(topic)
    narration = generate_voice_elevenlabs(script, f'{output_dir}/narration.mp3')
    keywords = topic.split()[:3]
    videos = search_pexels_videos(' '.join(keywords), count=3)
    clips = []
    for i, v in enumerate(videos):
        path = f'{output_dir}/clip_{i}.mp4'
        requests.get(v['url'], stream=True)  # download clip
        clips.append(path)
    subs = generate_subtitles(narration, f'{output_dir}/subs.srt')
    return assemble_video(clips, narration, subs, f'{output_dir}/final.mp4')

Examples

Example 1: Generate a Batch of Tech Fact Videos

A creator produces 5 technology-themed short videos for TikTok in one run:

topics = [
    "AI tools nobody talks about",
    "Apps that feel illegal to use for free",
    "Websites that will blow your mind",
    "Free AI tools every student needs",
    "Tech gadgets under  that changed my life"
]
for topic in topics:
    output = generate_video(topic, output_dir=f'./output/{topic[:30]}')
    print(f"Video ready: {output}")
# Each video: ~45 seconds, portrait 1080x1920, with subtitles and narration
# Total cost: ~
.25 (5 x 
.05 per video) using ElevenLabs + Claude Sonnet

Example 2: Daily Finance Shorts for YouTube

A finance channel automates daily Shorts upload with trending money topics:

import schedule

def daily_finance_video():
    topic = "3 passive income ideas that actually work in 2025"
    script = generate_script(topic, duration_seconds=55)
    # Script output:
    # HOOK: "You're losing money every single day you don't know about these."
    # BODY: 1. Print-on-demand stores (-2k/mo)
    #       2. AI-generated content licensing (-1k/mo)
    #       3. Dividend ETF stacking (-800/mo passive)
    # CTA: "Follow for more money tips that nobody tells you about."
    narration = generate_voice_openai(script, './daily/narration.mp3')
    videos = search_pexels_videos("money finance investing", count=4)
    # Downloads 4 portrait clips of money/charts/lifestyle footage
    # Assembles with bold white subtitles, outputs 55-second Short
    final = assemble_video(['./daily/clip_0.mp4', './daily/clip_1.mp4'],
                           narration, './daily/subs.srt', './daily/final.mp4')

schedule.every().day.at("08:00").do(daily_finance_video)

Guidelines

• Respect copyright — only use royalty-free stock footage (Pexels, Pixabay) or your own content
• Disclose AI usage — YouTube and TikTok require disclosure of AI-generated content
• Review before publishing — always watch the final video; AI scripts can contain inaccuracies
• Optimize for the first 3 seconds — the hook determines whether viewers stay or scroll
• Test multiple voices — ElevenLabs offers dozens of voices; find one that fits your niche
• Monitor performance — track views, retention, and click-through to iterate on content style

References

• MoneyPrinterTurbo — original inspiration (53k stars)
• Pexels API — free stock video
• ElevenLabs — realistic text-to-speech
• WhisperX — word-level subtitle alignment