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WhisperCUT

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TypeScript
Remote

WhisperCUT v3 — AI-Native Video Factory

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.

Quick Start (1 command)

Get your wcut_ token from the admin, then run:

curl -sL https://raw.githubusercontent.com/wallpapa/WhisperCUT/main/setup.sh | bash -s YOUR_TOKEN

The script will: verify token, clone, install, build, and configure Claude Code automatically.

Related MCP server: video-overlay-kit

How It Works

Topic + Vibe ──→ WhisperCUT ──→ Production-Ready MP4
                     │
         ┌───────────┼───────────┐
         │           │           │
    Vibe Engine  Hook Scorer  Voice Engine
    (AI Script)  (Gemini/    (MiniMax TTS)
                  Ollama)
         │           │           │
         └───────────┼───────────┘
                     │
              FFmpeg Render
           (1080x1920 @60fps)

5-Hormone Story Arc

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

5 Content Vibes

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"

17 MCP Tools

Vibe Engine (v3) — PRIMARY

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

Video Factory (v1)

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

Style Cloner (v2)

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

Autonomous Agent

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

P2P Network

Tool

Description

whispercut_p2p_status

Online workers, credit balance, leaderboard

whispercut_p2p_submit

Submit AI job to distributed network

P2P Distributed AI 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) ←┘

How It Works

  1. Your MCP server starts → registers as a worker

  2. You do your own work (80%)

  3. Network jobs come in → your worker picks up + processes (20%)

  4. You earn weighted credits for helping others

Credit System

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.

BYOK — Bring Your Own Key

Users provide their own AI API key. The system has zero AI costs.

Option A: Gemini Free (Recommended)

AI_PROVIDER=gemini
AI_API_KEY=your-key-from-aistudio.google.com

Get free key: https://aistudio.google.com/apikey (250 req/day)

Option B: Ollama Local (Free Forever)

brew install ollama && ollama pull gemma3:27b && ollama serve
AI_PROVIDER=ollama
AI_MODEL=gemma3:27b

Option C: OpenRouter Free Models

AI_PROVIDER=openrouter
AI_MODEL=google/gemma-3-27b-it:free
AI_API_KEY=sk-or-v1-your-key

Option D: Any OpenAI-Compatible API (GLM, Deepseek, etc.)

AI_PROVIDER=custom
AI_MODEL=glm-4-flash
AI_API_KEY=your-key
AI_BASE_URL=https://open.bigmodel.cn/api/paas/v4/

Architecture

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)

Research Foundation

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

Hook Taxonomy (6 types)

Type

Watch-Through Lift

CuriosityGap

+67%

SocialProofShock

+54%

VisualContrast

+48%

DirectAddress

+43%

BoldClaim

+41%

StoryOpening

+38%

Development

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)

Tech Stack

  • 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)

License

MIT

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Maintenance

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