The open source AI content engine with built-in niche intelligence.
Topic in. Published Short out. Any niche. ~$0.11 per video.
Repo note: the product is called Verticals v3. The GitHub repository is
youtube-shorts-pipeline.
python -m verticals run --topic "Sam Altman just mass-fired 200 safety researchers" --niche tech
That one command researches the topic, writes a hook driven script tuned to tech YouTube, generates cinematic b roll, records a natural voiceover, burns in animated captions, adds mood matched background music, generates a thumbnail, and uploads it to YouTube. ~90 seconds of video, ~3 minutes of wall time, ~$0.11 in API costs.
v2 was an esports news pipeline. v3 is a general purpose content engine that works for any niche, any topic, any creator.
The biggest change: Niche Intelligence. Every stage of the pipeline now reads from a niche profile that shapes script tone, visual style, caption aesthetics, music mood, and thumbnail strategy. Ship a cooking Short and it writes like a cooking creator, generates food photography b roll, and picks warm upbeat background music. Ship a true crime Short and the tone shifts to suspenseful, the visuals go dark and cinematic, and the music drops to ambient tension.
15 niches ship out of the box. Build your own in 5 minutes.
Other highlights: multi provider LLM support (Claude, Gemini, GPT, Ollama local), free TTS via Edge TTS, YouTube upload, topic discovery, resumable stages, and a local-first config model.
v3.0.1 is a credibility cleanup for the public repo after the first viral traffic spike. The docs now describe only the shipped local pipeline, the repo includes a physical MIT license file, and utility commands such as --help and --niches work without triggering first-run setup.
Implemented today:
- research with DuckDuckGo plus optional source scraping
- script and metadata generation through Claude, Gemini, GPT, Ollama, or Claude CLI
- b roll and thumbnail image generation through Gemini Imagen, with fallback frames
- voiceover through Edge TTS, ElevenLabs, or macOS
say - Whisper captions with ASS burn-in plus SRT export
- ffmpeg assembly with Ken Burns motion, background music, and voice ducking
- private-by-default YouTube upload
Not shipped yet: Gradio UI, Docker, Colab, TikTok/Reels/X upload, Pexels, Replicate, ComfyUI, and Kokoro TTS. Those are roadmap items, not current features.
┌─────────────────────────────────────────────────────────────────┐
│ NICHE PROFILE │
│ Loaded once. Shapes every stage. 15 built in or bring your own │
└─────────────┬───────────────────────────────────────────────────┘
│
▼
┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
│ RESEARCH │→ │ SCRIPT │→ │ VISUALS │→ │ VOICE │→ │ CAPTIONS │→ │ ASSEMBLE │→ UPLOAD
│ │ │ │ │ │ │ │ │ │ │ │
│ DuckDuck │ │ LLM with │ │ Gemini │ │ ElevenLabs│ │ Whisper │ │ ffmpeg │
│ Go + web │ │ niche │ │ fallback │ │ Edge TTS │ │ word │ │ Ken Burns│
│ scraping │ │ persona │ │ frames │ │ say │ │ level │ │ + music │
│ │ │ + hooks │ │ │ │ │ │ ASS+SRT │ │ ducking │
└──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘
Stage by stage:
Research — Searches DuckDuckGo (and optionally scrapes source URLs) for live facts. Every name, number, and claim in the final script traces back to this research. This is the anti hallucination gate: the LLM is instructed to use only facts from research data, never its training knowledge.
Script — An LLM (your choice of provider) writes a 60 to 90 second voiceover script using the niche profile's tone, pacing rules, and hook patterns. The profile tells the LLM things like "open with a question, not a statement" for tech niches or "open with a shocking statistic" for finance niches. Output includes the script, b roll image prompts, thumbnail prompt, and platform metadata for YouTube/TikTok/Instagram/X.
Visuals — Generates 3 b roll frames via Gemini Imagen, then auto crops them to 9:16 portrait. If image generation fails, the pipeline uses simple fallback frames so assembly can still complete. The niche profile shapes the visual vocabulary: a fitness niche generates gym and movement imagery, a science niche generates diagrams and lab visuals.
Voice — Text to speech via your configured provider: Edge TTS (free, cross platform, 300+ voices, recommended default), ElevenLabs (premium, most natural), or macOS say (fallback). The niche profile suggests voice characteristics (pace, energy, tone) but the final voice selection is yours.
Captions — Whisper generates word level timestamps. The pipeline produces both ASS (burned in with word by word yellow highlight) and SRT (uploaded to YouTube for closed captions). Caption styling follows the niche profile: bold energetic fonts for gaming, clean minimal for tech, warm handwritten feel for lifestyle.
Assemble — ffmpeg combines animated b roll (Ken Burns zoom/pan effects), voiceover, burned in captions, and background music with automatic voice ducking. Music selection is mood matched to the niche profile.
Upload — Publishes to YouTube (private by default) with title, description, tags, SRT captions, and AI generated thumbnail. TikTok and Instagram export coming in v3.1.
This is what makes Verticals different from every other AI video tool.
A niche profile is a YAML file that tells the pipeline how to think about content for a specific audience. It shapes every stage without requiring any prompt engineering from you.
# niches/tech.yaml
name: tech
display_name: "Tech & AI News"
script:
tone: "informed, slightly opinionated, conversational"
pacing: "fast, dense with facts, no filler"
hooks:
- pattern: "contrarian_take"
template: "Everyone is celebrating {topic}. Here's why that's a problem."
- pattern: "breaking_news"
template: "This just happened and nobody is talking about it."
- pattern: "prediction"
template: "{topic} changes everything. Here's what happens next."
- pattern: "explainer"
template: "Let me explain {topic} in 60 seconds because most people are getting this wrong."
- pattern: "comparison"
template: "{thing_a} vs {thing_b}. One of these wins and it's not even close."
cta_variants:
- "Follow for daily tech breakdowns."
- "Subscribe. I cover AI news nobody else is talking about."
- "Drop a comment: do you agree?"
word_count: "150 to 170"
forbidden: ["like and subscribe", "smash that bell", "what's up guys"]
visuals:
style: "clean, minimal, dark backgrounds, neon accents"
mood: "futuristic, sleek, professional"
subjects: ["circuit boards", "code on screens", "server rooms", "product shots", "data visualizations"]
avoid: ["stock photo people smiling at laptops", "generic office", "clipart"]
voice:
pace: "slightly fast, ~160 wpm"
energy: "confident, authoritative but not robotic"
suggested_voices:
edge_tts: "en-US-GuyNeural"
elevenlabs: "JBFqnCBsd6RMkjVDRZzb"
captions:
highlight_color: "#00FF88"
font_weight: "bold"
position: "lower_third"
music:
mood: "ambient electronic, subtle energy, no lyrics"
energy: "medium"
thumbnail:
style: "dark background, bold white/green text, product or face focus"
text_position: "left_aligned"15 built-in niches plus a general fallback: tech, gaming, finance, fitness, cooking, travel, true_crime, science, politics, entertainment, sports, fashion, education, motivation, comedy, and general.
Build your own by copying any profile and editing it. Drop the YAML in niches/ and reference it with --niche your_niche_name.
git clone https://github.com/rushindrasinha/youtube-shorts-pipeline.git
cd youtube-shorts-pipeline
pip install -r requirements.txt
python -m verticals run --topic "your topic" --niche techThe old --news flag still works for backwards compatibility, but --topic is the recommended public API.
Use verticals.gg if you want the workflow without local setup.
python -m verticals run --topic "headline" --niche tech
python -m verticals run --topic "headline" --niche cooking --provider ollama
python -m verticals run --discover --niche gaming --auto-pickpython -m verticals draft --topic "headline" --niche tech
python -m verticals produce --draft <path> --lang en
python -m verticals upload --draft <path> --lang en
python -m verticals topics --niche tech --limit 20--niche NAME Niche profile (default: general)
--provider NAME LLM provider: claude, gemini, openai, ollama (default: claude)
--voice NAME TTS provider: edge, elevenlabs, say (default: edge)
--platform NAME Draft target: shorts, reels, tiktok, all (default: shorts)
--lang CODE Language: en, hi, es, pt, de, fr, ja, ko (default: en)
--dry-run Draft only, skip produce and upload
--force Redo all stages even if completed
--verbose Debug logging
| Provider | Cost | Setup | Notes |
|---|---|---|---|
| Claude (Anthropic) | ~$0.02/script | ANTHROPIC_API_KEY |
Best quality. Default. |
| Gemini (Google) | Free tier available | GEMINI_API_KEY |
Good quality, generous free tier. |
| GPT (OpenAI) | ~$0.01/script | OPENAI_API_KEY |
Solid alternative. |
| Ollama (local) | Free | Install Ollama + pull model | No API key needed. Quality varies by model. |
| Claude CLI | Free w/ Max sub | Install Claude Code | Uses Claude Max subscription, no API key. |
| Provider | Cost | Setup | Notes |
|---|---|---|---|
| Edge TTS | Free | None | Recommended default. 300+ voices, cross platform. |
| ElevenLabs | ~$0.05/video | ELEVENLABS_API_KEY |
Most natural. Premium. |
| macOS say | Free | macOS only | Basic fallback. |
| Provider | Cost | Setup | Notes |
|---|---|---|---|
| Gemini Imagen | Free tier available | GEMINI_API_KEY |
Default image provider. |
| Fallback frames | Free | None | Solid-color fallback frames if image generation fails. |
| Platform | Status | Auth |
|---|---|---|
| YouTube | Stable | OAuth (setup wizard) |
| TikTok | v3.1 | Coming soon |
| Instagram Reels | v3.1 | Coming soon |
| X (Twitter) | v3.1 | Coming soon |
Yes, you can run this with zero API spend:
python -m verticals draft \
--topic "your topic" \
--niche tech \
--provider ollamaThis creates the script and metadata with a local LLM. Full video production still needs visuals, TTS, captions, and ffmpeg; Edge TTS is free, while Gemini visuals require GEMINI_API_KEY.
All keys stored in ~/.verticals/config.json with 0600 permissions:
| Variable | Required | Used By |
|---|---|---|
ANTHROPIC_API_KEY |
If using Claude | Script generation |
GEMINI_API_KEY |
If using Gemini visuals/LLM | B roll + thumbnails |
OPENAI_API_KEY |
If using GPT | Script generation |
ELEVENLABS_API_KEY |
If using ElevenLabs | Premium voiceover |
Environment variables override config file values.
Discover trending topics from multiple sources, filtered by niche relevance:
python -m verticals topics --niche tech --limit 20| Source | Method | Auth | Niche Filtering |
|---|---|---|---|
.json API |
None | Subreddit mapping per niche | |
| RSS | feedparser | None | Configurable feeds per niche |
| Google Trends | pytrends | None | Geo + category filtering |
| Twitter/X | Public API | Optional | Keyword filtering |
| TikTok | Apify | Optional | Hashtag mapping |
| YouTube Trending | RSS/API | None | Category mapping |
| Hacker News | API | None | Tech/startup default |
Configure per niche in your profile:
# In niches/tech.yaml
discovery:
reddit: ["technology", "artificial", "MachineLearning", "singularity"]
rss: ["https://hnrss.org/frontpage", "https://techcrunch.com/feed"]
google_trends_category: "t"
youtube_trending_category: "28"| Configuration | Cost |
|---|---|
| Premium (Claude + Gemini + ElevenLabs) | ~$0.11 |
| Budget (Gemini + Gemini + Edge TTS) | ~$0.04 |
| Draft-only local (Ollama) | $0.00 |
| Voice-only free path (Edge TTS) | $0.00 for voice generation |
Verticals is built for repeatable short-form production, not for replacing an editor on story-led creative work.
It works best for:
- news explainers
- niche trend breakdowns
- list-style educational Shorts
- fast social experiments where volume matters
It is not ideal for:
- cinematic storytelling
- creator personality pieces
- videos that require taste, live footage, or strong art direction
- factual topics where the research source quality is weak
Use draft --dry-run or run --dry-run when testing a new niche. The most important human checkpoint is the draft: hook, factual claims, and whether the video has a real reason to exist.
The pipeline is only the production layer. The useful operating loop is:
- Pick a niche with existing short-form demand.
- Generate and review a small batch of Shorts.
- Publish consistently on the platforms where that niche already moves.
- Use one clear call to action: free resource, newsletter, waitlist, or product.
- Read performance weekly, then tighten the niche profile and hooks.
This repo lowers the cost of testing. It does not remove the need for niche selection, distribution taste, or a real conversion path.
verticals/
├── verticals/
│ ├── __main__.py # CLI entry point
│ ├── config.py # Keys, paths, setup wizard
│ ├── niche.py # Niche profile loader
│ ├── llm.py # Claude / Gemini / GPT / Ollama
│ ├── research.py # DuckDuckGo research gate
│ ├── draft.py # Script generation with niche intelligence
│ ├── broll.py # Gemini image generation + Ken Burns
│ ├── tts.py # ElevenLabs / Edge / say
│ ├── captions.py # Whisper + ASS/SRT
│ ├── music.py # Track selection + ducking
│ ├── assemble.py # ffmpeg final assembly
│ ├── thumbnail.py # Thumbnail generation + text overlay
│ ├── upload.py # YouTube upload
│ ├── topics/ # Multi source topic engine
│ ├── state.py # Resume capability
│ ├── retry.py # Exponential backoff
│ └── log.py # Structured logging
├── niches/ # 15 built in niche profiles
│ ├── tech.yaml
│ ├── gaming.yaml
│ ├── finance.yaml
│ ├── fitness.yaml
│ ├── cooking.yaml
│ ├── travel.yaml
│ ├── true_crime.yaml
│ ├── science.yaml
│ ├── politics.yaml
│ ├── entertainment.yaml
│ ├── sports.yaml
│ ├── fashion.yaml
│ ├── education.yaml
│ ├── motivation.yaml
│ ├── comedy.yaml
│ └── general.yaml # Default fallback
├── tests/
├── scripts/
│ └── setup_youtube_oauth.py
├── references/
│ ├── setup.md
│ └── troubleshooting.md
├── pyproject.toml
└── requirements.txt
pip install -e ".[dev]"
python -m pytest tests/ -qAll security measures from v2 carry forward, plus:
Credential storage: Config and tokens use 0600 permissions via atomic os.open().
API key handling: All providers send keys via headers, never URL parameters.
Upload privacy: YouTube uploads default to private.
Prompt injection: Research snippets truncated to 300 chars with boundary markers. LLM output fields are type checked before use.
OAuth scopes: Minimum required scopes per platform.
Niche profiles: YAML parsed with safe_load (no code execution).
Dependency pinning: Compatible release bounds on all packages.
v3.0 (this release) Niche intelligence, multi provider LLM support, Edge TTS default, topic discovery, resumable stages, YouTube upload
v3.1 (planned) TikTok/Instagram/X upload, multi language niche profiles, A/B script variants (generate 2, pick better), scheduled batch production
v3.2 (planned) Analytics integration (which Shorts performed best), niche profile auto tuning based on performance data, series support (multi episode narrative arcs)
Later Web UI, Docker, Google Colab, additional visual providers, stock footage fallback
Dr Rushindra Sinha — MD, Stanford GSB, Full Stack Developer.
Built the first game server at 17 (went #1 globally, acquired before finishing med school). Co-founded Global Esports — South Asia's only Valorant Champions Tour Pacific franchise. Now building AI tools for creators and operators at aarees.com.
Follow: @irushi on X · @rushindrasinha on Instagram
| Product | What it does |
|---|---|
| verticals.gg | Hosted version of this pipeline — no setup, no terminal, just results |
| thumbnail.gg | AI thumbnail generation with deep niche intelligence and CTR optimization |
| aarees.com | The AI agent platform powering both products |
| Global Esports | South Asia's VCT Pacific franchise — where the esports niche profile was battle-tested |
MIT