Skillget

genpark-agentic-canary-deployment-drift-guard-skill

mcp
Security Audit
Warn
Health Warn
  • License — License: NOASSERTION
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 7 GitHub stars
Code Pass
  • Code scan — Scanned 4 files during light audit, no dangerous patterns found
Permissions Pass
  • Permissions — No dangerous permissions requested

No AI report is available for this listing yet.

SUMMARY

Agentic Canary Deployment Drift & Metric Regression Guard. Conducts Welch's t-test statistical hypothesis testing between baseline and canary agent versions across P99 latency, error rates, and hallucination scores to execute instant automa | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-agentic-canary-deployment-drift-guard-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Agentic Canary Deployment Drift & Metric Regression Guard. Conducts Welch's t-test statistical hypothesis testing between baseline and canary agent versions across P99 latency, error rates, and hallucination scores to execute instant automated rollback circuit breakers.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import AgenticCanaryDeploymentDriftGuard

def main():
    guard = AgenticCanaryDeploymentDriftGuard()
    baseline = {
        "latency_ms": [85.0, 88.0, 92.0, 87.0, 89.0],
        "error_rate": [0.005, 0.002, 0.004, 0.003],
        "hallucination_score": [0.02, 0.03, 0.01, 0.02]
    }
    canary = {
        "latency_ms": [86.0, 87.0, 90.0, 88.0, 88.5],
        "error_rate": [0.004, 0.003, 0.002, 0.003],
        "hallucination_score": [0.02, 0.01, 0.02, 0.02]
    }
    res = guard.evaluate_canary_health(baseline, canary)
    print("--- Canary Deployment Health Evaluation ---")
    print(f"Decision: {res['overall_decision']}")
    print(f"Action: {res['recommended_action']}")
    for k, v in res['metrics_breakdown'].items():
        print(f"  - {k}: Base={v['baseline_mean']} vs Canary={v['canary_mean']} (Delta: {v['percentage_delta']}%, Status: {v['status']})")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

Reviews (0)

No results found