Provides flaky test detection by analyzing test suite traces and telemetry from Datadog CI to identify timing races and state pollution.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genpark-flaky-test-timing-non-determinism-detector-skill Analyze this test suite trace for timing races and state pollution."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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genpark-flaky-test-timing-non-determinism-detector-skill is a deterministic, zero-dependency Python skill engineered for autonomous AI code reviews, AST-level taint flow tracing, breaking API change audits, and test flakiness mitigation.
Executive Capability: Automated test suite trace analyzer identifying timing races & state pollution (Datadog CI / Trunk)
🐍 Zero External pip Dependencies: Runs instantaneously on standard Python 3.9+ with zero environment bloat.
🔌 Native Model Context Protocol (MCP): Seamlessly integrates into Claude Desktop, Cursor IDE, GitHub Actions PR bots, and autonomous developer workflows.
🎯 Deterministic & Reliable: 100% predictable input/output contracts with full JSON Schema validation.
🚀 Low Latency: Sub-millisecond execution overhead tailored for high-frequency CI/CD pipelines.
Related MCP server: flakiness-knowledge-graph-mcp
graph LR
User([💻 Pull Request / Developer]) -->|Diff & AST Source Code| MCP[⚡ MCP Server / Protocol]
MCP --> Client[🛠️ Code Review Sentinel Kernel]
Client --> Analyzer[🧠 AST & Taint Flow Analysis Pipeline]
Analyzer --> Dossier[📊 Review Report & Breaking Change Alerts]
Dossier --> Userpython example_usage.pyfrom client import FlakyTestTimingNonDeterminismDetectorClient
client = FlakyTestTimingNonDeterminismDetectorClient()
result = client.analyze_flaky_test_telemetry()
print(result)Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json{
"mcpServers": {
"genpark-flaky-test-timing-non-determinism-detector-skill": {
"command": "python",
"args": ["/path/to/genpark-flaky-test-timing-non-determinism-detector-skill/mcp_server.py"]
}
}
}Parameter | Type | Required | Description |
|
| Yes | Primary input parameter parsed and executed deterministically |
|
| Yes | Standardized response schema containing execution telemetry |
GenPark 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.
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Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.
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