AI agents operate as creative directors + editors. One command. One topic. Production-ready TikTok video.
WhisperCUT is an MCP server with 17 tools that automates short-form video production for TikTok, Instagram Reels, and YouTube Shorts. Every creative decision — hooks, pacing, transitions, CTAs — is driven by behavioral science research, not artistic intuition.
Get your wcut_ token from the admin, then run:
curl -sL https://raw.githubusercontent.com/wallpapa/WhisperCUT/main/setup.sh | bash -s YOUR_TOKENThe script will: verify token, clone, install, build, and configure Claude Code automatically.
Topic + Vibe ──→ WhisperCUT ──→ Production-Ready MP4
│
┌───────────┼───────────┐
│ │ │
Vibe Engine Hook Scorer Voice Engine
(AI Script) (Gemini/ (MiniMax TTS)
Ollama)
│ │ │
└───────────┼───────────┘
│
FFmpeg Render
(1080x1920 @60fps)
Every video follows a neuroscience-backed emotional arc:
| Time | Hormone | Purpose | Example |
|---|---|---|---|
| 0-3s | Cortisol | Threat/tension hook | "ถ้าลูกคุณอายุ 3 ขวบ อ่านด่วน!" |
| 3-15s | Dopamine | Curiosity gap | "8/10 ครอบครัว ทำสิ่งนี้โดยไม่รู้ตัว" |
| 15-35s | Oxytocin | Trust building | Personal story, vulnerability |
| 35-55s | Adrenaline | Peak moment | Counter-intuitive revelation |
| 55-75s | Serotonin | Resolution + CTA | Actionable advice + save prompt |
| Vibe | Completion | Share | Best For |
|---|---|---|---|
educational_warm |
71% | 6.4% | Expert knowledge sharing |
shocking_reveal |
74% | 8.3% | Myth-busting, bold claims |
story_driven |
68% | 9.1% | Family/child narratives |
quick_tips |
77% | 7.1% | Fast-paced lists |
myth_bust |
73% | 7.6% | "Truth nobody tells you" |
| Tool | Description |
|---|---|
whispercut_vibe_edit |
One-call video production: topic + vibe → MP4 with script, hook scoring, QA |
whispercut_list_vibes |
List 5 vibes with predicted completion/share rates |
| Tool | Description |
|---|---|
whispercut_analyze |
Transcribe video with Whisper + AI analysis |
whispercut_cut |
Generate cut list from analysis |
whispercut_caption |
Burn animated Thai subtitles via FFmpeg |
whispercut_render |
Full 9:16 1080x1920 @60fps H.264 render |
whispercut_export_capcut |
Export timeline as CapCut draft |
whispercut_publish |
Upload to TikTok via session auth |
whispercut_feedback |
AI quality scoring + iterative improvement |
| Tool | Description |
|---|---|
whispercut_study |
Analyze TikTok channel → extract style template |
whispercut_clone |
Generate script from style template + topic |
whispercut_capcut_clone |
Export clone script as CapCut draft |
| Tool | Description |
|---|---|
whispercut_run_pipeline |
Full pipeline: study → script → QA → voice → render → publish |
whispercut_schedule |
Add topic to content calendar for scheduled run |
whispercut_status |
Today's quota, upcoming jobs, recent results |
| Tool | Description |
|---|---|
whispercut_p2p_status |
Online workers, credit balance, leaderboard |
whispercut_p2p_submit |
Submit AI job to distributed network |
Users contribute 20% of their AI processing power to a shared pool. More users = more capacity = better service for everyone.
User A (Gemini) ←──┐
│ Supabase Realtime
User B (Ollama GPU) ←┼──→ Job Queue ←──→ Credit Ledger
│
User C (OpenRouter) ←┘
- Your MCP server starts → registers as a worker
- You do your own work (80%)
- Network jobs come in → your worker picks up + processes (20%)
- You earn weighted credits for helping others
| Job Type | Credits | Weight |
|---|---|---|
hook_score |
1 | Light — score a hook |
weekly_plan |
2 | Medium — generate content plan |
qa_gate |
3 | Medium — review script quality |
vibe_script |
5 | Heavy — generate full script |
New users get 10 free credits on signup.
Users provide their own AI API key. The system has zero AI costs.
AI_PROVIDER=gemini
AI_API_KEY=your-key-from-aistudio.google.comGet free key: https://aistudio.google.com/apikey (250 req/day)
brew install ollama && ollama pull gemma3:27b && ollama serveAI_PROVIDER=ollama
AI_MODEL=gemma3:27bAI_PROVIDER=openrouter
AI_MODEL=google/gemma-3-27b-it:free
AI_API_KEY=sk-or-v1-your-keyAI_PROVIDER=custom
AI_MODEL=glm-4-flash
AI_API_KEY=your-key
AI_BASE_URL=https://open.bigmodel.cn/api/paas/v4/src/
├── mcp/ # MCP server + 17 tool handlers
│ ├── server.ts # Main entry (stdio transport, v3.1.0)
│ └── tools/ # One file per tool domain
├── engine/ # Core production engines
│ ├── vibe-engine # AI script generation (hormone arc)
│ ├── ffmpeg # Video rendering (1080x1920 @60fps)
│ ├── whisper # Audio transcription (Thai)
│ ├── voice # MiniMax TTS (Dr.Gwang clone)
│ ├── timeline # Timeline composition
│ └── capcut # CapCut draft export
├── science/ # Behavioral science algorithms
│ ├── hook-scorer # 6-taxonomy hook evaluation
│ ├── cta-selector # Conversion-optimized CTA
│ └── vibe-library # 5 research-encoded vibes
├── ai/ # Unified AI provider (BYOK)
│ ├── provider # Gemini/OpenRouter/Ollama/Custom gateway
│ ├── prompts # Prompt templates
│ └── feedback-loop # Auto-improve cycle
├── agent/ # Autonomous orchestration
│ ├── pipeline # 8-stage production pipeline
│ ├── qa-gate # Quality gate (7.5/10 threshold)
│ ├── scheduler # Content calendar + weekly AI plan
│ └── rate-limiter # Multi-platform quota tracking
├── p2p/ # Distributed AI network
│ ├── worker # Realtime job processing daemon
│ ├── submitter # Job submission + fallback
│ └── credits # Weighted credit system
└── db/ # Supabase client + schema (10 tables)
All creative decisions are encoded from peer-reviewed research:
- Dopamine Prediction Error — Schultz et al., 1997
- Narrative Transportation Theory — Green & Brock, 2000
- Fogg Behavior Model — BJ Fogg, 2009
- Zeigarnik Effect — Unresolved tension drives completion
- TikTok Creator Academy — Platform algorithm research (2022-2025)
- 10K+ Video Dataset — Completion rate predictions
| Type | Watch-Through Lift |
|---|---|
| CuriosityGap | +67% |
| SocialProofShock | +54% |
| VisualContrast | +48% |
| DirectAddress | +43% |
| BoldClaim | +41% |
| StoryOpening | +38% |
git clone https://github.com/wallpapa/WhisperCUT.git
cd WhisperCUT
npm install
npm run build
npm start # stdio MCP server
npm run dev # hot reload
npx tsx test_e2e.ts # E2E test (8 layers)- Runtime: Node.js 22+ / TypeScript (strict, ES2022)
- MCP: @modelcontextprotocol/sdk v1.12.1 (stdio)
- AI: Vercel AI SDK + @ai-sdk/openai-compatible
- Database: Supabase (Postgres + Realtime + Storage)
- Video: FFmpeg (H.264, 1080x1920, 60fps, Thai font)
- TTS: MiniMax (Dr.Gwang cloned voice)
MIT