Analyzing Test Coverage
by jeremylongshore
|
Skill Details
Repository Files
1 file in this skill directory
name: Analyzing Test Coverage description: | This skill analyzes code coverage metrics to identify untested code and generate comprehensive coverage reports. It is triggered when the user requests analysis of code coverage, identification of coverage gaps, or generation of coverage reports. The skill is best used to improve code quality by ensuring adequate test coverage and identifying areas for improvement. Use trigger terms like "analyze coverage", "code coverage report", "untested code", or the shortcut "cov".
Overview
This skill enables Claude to analyze code coverage metrics, pinpoint areas of untested code, and generate detailed reports. It helps you identify gaps in your test suite and ensure comprehensive code coverage.
How It Works
- Coverage Data Collection: Claude executes the project's test suite with coverage tracking enabled (e.g., using
nyc,coverage.py, or JaCoCo). - Report Generation: The plugin parses the coverage data and generates a detailed report, including metrics for line, branch, function, and statement coverage.
- Uncovered Code Identification: Claude highlights specific lines or blocks of code that are not covered by any tests.
When to Use This Skill
This skill activates when you need to:
- Analyze the overall code coverage of your project.
- Identify specific areas of code that lack test coverage.
- Generate a detailed report of code coverage metrics.
- Enforce minimum code coverage thresholds.
Examples
Example 1: Analyzing Project Coverage
User request: "Analyze code coverage for the entire project"
The skill will:
- Execute the project's test suite with coverage tracking.
- Generate a comprehensive coverage report, showing line, branch, and function coverage.
Example 2: Identifying Untested Code
User request: "Show me the untested code in the src/utils.js file"
The skill will:
- Analyze the coverage data for
src/utils.js. - Highlight the lines of code in
src/utils.jsthat are not covered by any tests.
Best Practices
- Configuration: Ensure your project has a properly configured coverage tool (e.g.,
nycin package.json). - Thresholds: Define minimum coverage thresholds to enforce code quality standards.
- Report Review: Regularly review coverage reports to identify and address coverage gaps.
Integration
This skill can be integrated with other testing and CI/CD tools to automate coverage analysis and reporting. For example, it can be used in conjunction with a linting plugin to identify both code style issues and coverage gaps.
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.
