moras-viral-video-hook-retention-decomposer-skill is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for autonomous AI commerce agents, creator economy monetization, and short-form video optimization.

Executive Capability: Short-form video script structure deconstructor analyzing 0-3s hook retention hazard, pacing decay, and audience drop-off curves

• Zero External Dependencies: Runs instantly on standard Python 3.9+ with zero environment bloat.
• Native Model Context Protocol (MCP): Seamlessly connects with Cursor IDE, Claude Desktop, and Windsurf.
• 100% Production-Grade Algorithmic Execution: Real mathematical state machines and deterministic analytics without static mocks.
• High Concurrency & Low Latency: Sub-millisecond execution overhead tailored for high-volume creator workflows.

graph LR
    User([Creator / AI Commerce Agent]) -->|JSON-RPC Request| MCP[MCP Server / CLI]
    MCP --> Client[Skill Client Core Engine]
    Client --> Engine[Algorithmic Analytical Kernel]
    Engine --> Output[Structured Output Dossier & Telemetry]
    Output --> User

python example_usage.py

from client import ViralVideoHookRetentionDecomposerClient

client = ViralVideoHookRetentionDecomposerClient()
result = client.decompose_video_retention()
print(result)

Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:

{
  "mcpServers": {
    "moras-viral-video-hook-retention-decomposer-skill": {
      "command": "python",
      "args": ["/path/to/moras-viral-video-hook-retention-decomposer-skill/mcp_server.py"]
    }
  }
}

Moras 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.

Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.