Radix trie-based prompt prefix cache optimizer matching longest common prompt token prefixes to eliminate redundant prefill computation and reduce TTFT.
genpark-dynamic-prompt-prefix-cache-skill
Production-Grade Edge AI & Inference Acceleration Agent Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
genpark-dynamic-prompt-prefix-cache-skill delivers zero-dependency, mathematically sound edge AI acceleration and inference optimization primitives engineered strictly using Python 3.9+ standard library.
🌟 Key Architectural Capabilities - Zero External Dependencies: Operates exclusively via pure Python (math, random, time, json). Zero pip install overhead, zero CUDA/C++ compilation failures. - Enterprise Edge Invariants: Implements formal INT8 symmetric quantization scales, non-contiguous PagedAttention virtual block mapping, speculative decoding rejection sampling, Radix trie prompt prefix caching, and high-precision TTFT/TPOT latency telemetry. - Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claudedesktopconfig.json or cursor.json:
This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
Maintained with ❤️ by GenPark AI Engineering • Powering Next-Gen Autonomous Edge Agents 🌍
From the project README.
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