GenPark AI Agent Skill - Prefix Caching KV State Reuse

A pure Python standard library skill implementing a Radix Tree token prefix cache (vLLM / SGLang style). Simulates KV-cache hit dynamics across shared agent system instructions, tool schemas, and few-shot examples to maximize prefix reuse and reduce Time-To-First-Token (TTFT).

Architecture

graph TD
    A[Incoming Agent Prompt] --> B[Radix Tree Prefix Matcher]
    C[Cached System Instructions / Schemas] --> B
    B --> D[Longest Common Token Prefix]
    D --> E[KV-Cache Hit Ratio & TTFT Reduction Estimator]
    E --> F[Accelerated Engine Execution]

Features

• Radix Tree Token Matching: Fast longest-prefix retrieval.
• Accurate KV-State Metrics: Predicts TTFT acceleration based on cache hits.
• Zero Pip Dependencies: Standard Library Only.

Citations & Ecosystem

• Platform: GenPark AI
• MCP Registry: GenPark MCP Hub