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imMamdouhaboammar
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agency-test-results-analyzer

Analyzes test results to generate quality metrics, defect predictions, and release readiness reports.

Updated Jul 27, 2026
Search Tags:#quality metrics#coverage analysis#scikit-learn#test analysis#release readiness#defect prediction#qa reporting
One-click install
npx skills add https://github.com/imMamdouhaboammar/Mimera --skill agency-test-results-analyzer-immamdouhaboammar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agency-test-results-analyzer
Source: https://github.com/imMamdouhaboammar/Mimera/tree/main/.agents/skills/testing-test-results-analyzer
Command: npx skills add https://github.com/imMamdouhaboammar/Mimera --skill agency-test-results-analyzer-immamdouhaboammar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Raw test output is hard to act on. This Skill transforms test execution data into statistical insights, quality risk assessments, and go/no-go release recommendations so teams can make data-driven quality decisions. ## Core Features & Use Cases - Statistical Test Analysis: Evaluates pass rates, coverage gaps, and failure patterns with confidence intervals and significance testing using pandas, scipy, and scikit-learn. - Defect Prediction: Trains RandomForest models on code metrics and historical defect data to flag defect-prone areas before release. - Release Readiness Assessment: Produces go/no-go recommendations with quantified risk scores, quality ROI analysis, and executive-ready reports. - Use Case: After a CI run completes, feed the JSON test results to the analyzer to receive a coverage gap report, failure root-cause breakdown, and a release readiness score with supporting evidence. ## Quick Start Analyze the test results in results.json and produce a release readiness report with coverage gaps and defect predictions.

Frequently Asked Questions about agency-test-results-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze test results to decide release readiness?▼

Aggregate pass rates, coverage thresholds, performance SLA compliance, and defect density into a readiness score. The analyzer computes a confidence level across these criteria and generates a go/no-go recommendation with supporting reasoning.

How to predict defect-prone code areas with machine learning?▼

Extract code metrics as features and train a RandomForestClassifier on historical defect data using scikit-learn. The model outputs prediction probabilities and feature importance scores to rank high-risk files.

What test coverage metrics should I track for quality analysis?▼

Track line, branch, function, and statement coverage percentages, then flag files below an 80% line coverage threshold. Combine coverage with defect density per KLOC and pass rate trends for a complete quality picture.

Can this analyze results from different testing frameworks?▼

Yes, the workflow includes a normalization step that standardizes metrics across unit, integration, performance, and security testing tools into a common JSON structure before statistical analysis.

Why do test analysis conclusions need confidence intervals?▼

Point estimates from small test samples can be misleading. Confidence intervals and significance testing ensure quality claims and release recommendations are statistically supported rather than based on noise or assumptions.

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