Production-Grade AI Media & Video Processing Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
🌐 GenPark MCP Hub Showcase • 📦 GenPark Official Website • 📖 Documentation
genpark-shortform-hook-retention-scorer-skill is a deterministic, zero-dependency Python skill engineered for autonomous shortform video editing, audio silence pacing, generative canvas tiling, and viral hook optimization.
Executive Capability: First-3-second shortform video hook retention scorer (Submagic / Opus Clip)
- 🐍 Zero External
pipDependencies: Runs instantaneously on standard Python 3.9+ with zero environment bloat. - 🔌 Native Model Context Protocol (MCP): Seamlessly integrates into Claude Desktop, Cursor IDE, CapCut bot automations, and viral video agent pipelines.
- 🎯 Deterministic & Reliable: 100% predictable input/output contracts with full JSON Schema validation.
- 🚀 Low Latency: Sub-millisecond execution overhead tailored for high-concurrency batch media processing.
graph LR
User([🎬 Video Creator / Media Swarm]) -->|Audio/Video Telemetry & Transcript| MCP[⚡ MCP Server / Protocol]
MCP --> Client[🛠️ Media Processing Kernel]
Client --> Engine[🧠 Pacing & Reframing Pipeline]
Engine --> Cuts[✂️ Precise Cut-Lists & Telemetry Dossier]
Cuts --> User
python example_usage.pyfrom client import ShortformHookRetentionScorerClient
client = ShortformHookRetentionScorerClient()
result = client.score_video_hook()
print(result)Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
{
"mcpServers": {
"genpark-shortform-hook-retention-scorer-skill": {
"command": "python",
"args": ["/path/to/genpark-shortform-hook-retention-scorer-skill/mcp_server.py"]
}
}
}| Parameter | Type | Required | Description |
|---|---|---|---|
query_payload |
string / dict |
Yes | Primary input parameter parsed and executed deterministically |
output_format |
json / dict |
Yes | Standardized response schema containing execution telemetry |
GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.
Explore the comprehensive directory of 1,200+ open-source, production-ready AI Agent skills at the GenPark AI MCP Hub and learn more about multimedia AI tools at GenPark AI.
Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.