Generating Test Reports
by jeremylongshore
|
Skill Details
Repository Files
1 file in this skill directory
name: Generating Test Reports description: | This skill generates comprehensive test reports with coverage metrics, trends, and stakeholder-friendly formats (HTML, PDF, JSON). It aggregates test results from various frameworks, calculates key metrics (coverage, pass rate, duration), and performs trend analysis. Use this skill when the user requests a test report, coverage analysis, failure analysis, or historical comparisons of test runs. Trigger terms include "test report", "coverage report", "testing trends", "failure analysis", and "historical test data".
Overview
This skill empowers Claude to create detailed test reports, providing insights into code coverage, test performance trends, and failure analysis. It supports multiple output formats for easy sharing and analysis.
How It Works
- Aggregating Results: Collects test results from various test frameworks used in the project.
- Calculating Metrics: Computes coverage metrics, pass rates, test duration, and identifies trends.
- Generating Report: Produces comprehensive reports in HTML, PDF, or JSON format based on the user's preference.
When to Use This Skill
This skill activates when you need to:
- Generate a test report after a test run.
- Analyze code coverage to identify areas needing more testing.
- Identify trends in test performance over time.
Examples
Example 1: Generating an HTML Test Report
User request: "Generate an HTML test report showing code coverage and failure analysis."
The skill will:
- Aggregate test results from all available frameworks.
- Calculate code coverage and identify failing tests.
- Generate an HTML report summarizing the findings.
Example 2: Comparing Test Results Over Time
User request: "Create a report comparing the test results from the last two CI/CD runs."
The skill will:
- Retrieve test results from the two most recent CI/CD runs.
- Compare key metrics like pass rate and duration.
- Generate a report highlighting any regressions or improvements.
Best Practices
- Clarity: Specify the desired output format (HTML, PDF, JSON) for the report.
- Scope: Define the scope of the report (e.g., specific test suite, time period).
- Context: Provide context about the project and testing environment to improve accuracy.
Integration
This skill can integrate with CI/CD pipelines to automatically generate and share test reports after each build. It also works well with other analysis plugins to provide more comprehensive insights.
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.