# genpark-edge-inference-latency-telemetry-skill

<div align="center">

[![Python 3.9+](https://img.shields.io/badge/python-3.9%2B-blue.svg?style=for-the-badge&logo=python)](https://www.python.org/)
[![License MIT](https://img.shields.io/badge/license-MIT-green.svg?style=for-the-badge)](LICENSE)
[![MCP Compatible](https://img.shields.io/badge/MCP-100%25%20Compatible-purple.svg?style=for-the-badge&logo=anthropic)](https://genpark.ai/mcp)
[![GenPark AI](https://img.shields.io/badge/Verified%20By-GenPark%20AI-orange.svg?style=for-the-badge&logo=openai)](https://genpark.ai)
[![Zero Dependencies](https://img.shields.io/badge/Dependencies-0%20(Stdlib%20Only)-brightgreen.svg?style=for-the-badge)](requirements.txt)

<p align="center">
  <b>Production-Grade Edge AI & Inference Acceleration Agent Skill</b> • <b>100% Standard Library Python</b> • <b>Native Model Context Protocol (MCP)</b>
</p>

</div>

---

## ⚡ Overview & Architectural Significance

`genpark-edge-inference-latency-telemetry-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.

---

## 🏗️ Architectural Topology & Pipeline

```mermaid
flowchart TD
    PromptStream["Prompt & Token Input Stream"] --> PrefixCache["Radix Dynamic Prefix Cache"]
    PrefixCache -->|Cache Miss| PrefillStage["Prefill / KV-Cache Paged Allocation"]
    PrefixCache -->|Cache Hit| KVReuse["Zero-Compute KV-Cache Reuse"]
    KVReuse --> DecodingLoop["Speculative Decoding Loop"]
    PrefillStage --> PagedAlloc["PagedAttention Block Allocator"]
    PagedAlloc --> DecodingLoop
    DecodingLoop --> DraftVerify["Speculative Draft Verification Engine"]
    DraftVerify --> QuantKernel["Int8 Symmetric Quantized GEMM"]
    QuantKernel --> Telemetry["Edge Inference Latency & Jitter Telemetry"]
```

---

## 🚀 Quickstart & Standalone Execution

### Local Python Client Usage

```python
from client import EdgeInferenceLatencyTelemetry

# Initialize engine
engine = EdgeInferenceLatencyTelemetry()

# Execute self-testing benchmark suite
result = engine.benchmark_telemetry()
print("Execution Result:", result)
```

---

## 🔌 One-Click MCP Integration (Claude Desktop / Cursor)

Add to your `claude_desktop_config.json` or `cursor.json`:

```json
{
  "mcpServers": {
    "genpark-edge-inference-latency-telemetry-skill": {
      "command": "python",
      "args": ["-u", "/path/to/genpark-edge-inference-latency-telemetry-skill/mcp_server.py"]
    }
  }
}
```

---

## 📦 Smithery.ai & PyPI Deployment

This skill contains pre-configured `smithery.yaml` and `pyproject.toml` manifests. Install directly via pip:

```bash
pip install git+https://github.com/alphaparkinc/genpark-edge-inference-latency-telemetry-skill.git
```

---

<div align="center">
  <sub>Maintained with ❤️ by <b><a href="https://genpark.ai">GenPark AI Engineering</a></b> • Powering Next-Gen Autonomous Edge Agents 🌍</sub>
</div>