ReddRadar
Agent skill
pytest-ml-tester
ML-specific testing skill using pytest with fixtures for data, models, and predictions.
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/data-science-ml/skills/pytest-ml-tester
SKILL.md
pytest-ml-tester
Overview
ML-specific testing skill using pytest with specialized fixtures for data validation, model loading, prediction testing, and ML pipeline verification.
Capabilities
- Data validation fixtures
- Model loading fixtures
- Prediction testing utilities
- Performance regression tests
- Integration test helpers
- Coverage reporting for ML code
- Property-based testing with Hypothesis
- Parameterized test generation
Target Processes
- ML System Integration Testing
- Model Evaluation and Validation Framework
- Data Collection and Validation Pipeline
Tools and Libraries
- pytest
- pytest-cov
- hypothesis
- great-expectations (optional)
Input Schema
{
"type": "object",
"required": ["action"],
"properties": {
"action": {
"type": "string",
"enum": ["run", "generate", "coverage", "fixtures"],
"description": "Testing action to perform"
},
"testConfig": {
"type": "object",
"properties": {
"testPath": { "type": "string" },
"markers": { "type": "array", "items": { "type": "string" } },
"verbose": { "type": "boolean" },
"failFast": { "type": "boolean" },
"parallel": { "type": "integer" }
}
},
"coverageConfig": {
"type": "object",
"properties": {
"sourcePath": { "type": "string" },
"minCoverage": { "type": "number" },
"reportFormat": { "type": "string", "enum": ["html", "xml", "term"] }
}
},
"generateConfig": {
"type": "object",
"properties": {
"testType": {
"type": "string",
"enum": ["data_validation", "model_inference", "performance", "integration"]
},
"targetPath": { "type": "string" },
"modelPath": { "type": "string" },
"dataPath": { "type": "string" }
}
},
"fixtureConfig": {
"type": "object",
"properties": {
"dataFixtures": { "type": "array" },
"modelFixtures": { "type": "array" },
"scope": { "type": "string", "enum": ["function", "class", "module", "session"] }
}
}
}
}
Output Schema
{
"type": "object",
"required": ["status", "results"],
"properties": {
"status": {
"type": "string",
"enum": ["passed", "failed", "error"]
},
"results": {
"type": "object",
"properties": {
"totalTests": { "type": "integer" },
"passed": { "type": "integer" },
"failed": { "type": "integer" },
"skipped": { "type": "integer" },
"duration": { "type": "number" }
}
},
"failures": {
"type": "array",
"items": {
"type": "object",
"properties": {
"testName": { "type": "string" },
"error": { "type": "string" },
"traceback": { "type": "string" }
}
}
},
"coverage": {
"type": "object",
"properties": {
"percentage": { "type": "number" },
"reportPath": { "type": "string" },
"uncoveredLines": { "type": "object" }
}
},
"generatedTests": {
"type": "array",
"items": { "type": "string" }
}
}
}
Usage Example
{
kind: 'skill',
title: 'Run ML integration tests',
skill: {
name: 'pytest-ml-tester',
context: {
action: 'run',
testConfig: {
testPath: 'tests/integration/',
markers: ['integration', 'model'],
verbose: true,
parallel: 4
},
coverageConfig: {
sourcePath: 'src/',
minCoverage: 80,
reportFormat: 'html'
}
}
}
}
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