F
licenseNot graded
qualityC
maintenanceEnables comprehensive statistical analysis including descriptive statistics, hypothesis testing, regression, and more via a FastMCP-based API.
3
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genpark-hypothesis-welch-t-test-statistical-evaluator-skill
Officialby Alpha-Park
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-hypothesis-welch-t-test-statistical-evaluator-skillrun welch's t-test on these two samples: [1,2,3] and [4,5,6]"
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.
genpark-hypothesis-welch-t-test-statistical-evaluator-skill
⚡ Overview & Architectural Significance
genpark-hypothesis-welch-t-test-statistical-evaluator-skill delivers zero-dependency, mathematically rigorous statistical modeling, time-series forecasting, and anomaly detection primitives engineered strictly using Python 3.9+ standard library.
🌟 Key Architectural Capabilities
Zero External Dependencies: Operates exclusively via pure Python (
math,random,statistics,json). Zero pip install overhead, zero numpy/scipy compilation failures.Enterprise Statistical Invariants: Implements formal Holt linear smoothing, modified Z-score & Tukey IQR anomaly bounds, Welch's t-test p-value estimations, multivariate linear regression via gradient descent, and Power Iteration PCA dimensionality reduction.
Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
Related MCP server: genpark-statistical-zscore-iqr-anomaly-detector-skill
🏗️ Architectural Topology & State Machine
flowchart TD
DataStream["Time-Series & Multidimensional Numerical Stream"] --> AnomalyDetector["Statistical Z-Score & IQR Anomaly Detector"]
AnomalyDetector -->|Outlier Detected| FlagAnomaly["Flag Outlier & Alert Telemetry"]
AnomalyDetector -->|Clean Stream| ModelingBranch{"Analytical Objective"}
ModelingBranch -->|Trend Forecasting| HoltForecaster["Holt Linear Exponential Smoothing Forecaster"]
ModelingBranch -->|Supervised Fitting| LinearRegression["Multivariate Gradient Descent Regression Engine"]
ModelingBranch -->|Hypothesis Testing| WelchTest["Welch's Two-Sample T-Test Evaluator"]
ModelingBranch -->|Dimensionality Reduction| PCAReducer["Power Iteration SVD/PCA Dimension Reducer"]
HoltForecaster --> Synthesis["Agent Synthesis & Statistical Report"]
LinearRegression --> Synthesis
WelchTest --> Synthesis
PCAReducer --> Synthesis🚀 Quickstart & Standalone Execution
Local Python Client Usage
from client import HypothesisWelchTTestEvaluator
# Initialize engine
engine = HypothesisWelchTTestEvaluator()
# Execute self-testing benchmark suite
result = engine.run_benchmark_welch_t_test()
print("Execution Result:", result)🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-hypothesis-welch-t-test-statistical-evaluator-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-hypothesis-welch-t-test-statistical-evaluator-skill/mcp_server.py"]
}
}
}📦 Smithery.ai & PyPI Deployment
This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
pip install git+https://github.com/alphaparkinc/genpark-hypothesis-welch-t-test-statistical-evaluator-skill.gitThis server cannot be deployed
Maintenance
ActivityMaintained
ResponsivenessNo issues
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