pytest-coverage

$npx mdskill add github/awesome-copilot/pytest-coverage

Run pytest tests with coverage analysis to identify and fix uncovered code lines, aiming for 100% coverage.

  • Helps developers ensure all code lines are tested by pinpointing gaps in test coverage.
  • Integrates with pytest and coverage tools to generate annotated reports for source files.
  • Recommends adding tests based on lines marked with exclamation marks in coverage reports.
  • Presents results through annotated files in a directory, highlighting uncovered lines for review.

SKILL.md

.github/skills/pytest-coverageView on GitHub ↗
---
name: pytest-coverage
description: 'Run pytest tests with coverage, discover lines missing coverage, and increase coverage to 100%.'
---

The goal is for the tests to cover all lines of code.

Generate a coverage report with:

pytest --cov --cov-report=annotate:cov_annotate

If you are checking for coverage of a specific module, you can specify it like this:

pytest --cov=your_module_name --cov-report=annotate:cov_annotate

You can also specify specific tests to run, for example:

pytest tests/test_your_module.py --cov=your_module_name --cov-report=annotate:cov_annotate

Open the cov_annotate directory to view the annotated source code.
There will be one file per source file. If a file has 100% source coverage, it means all lines are covered by tests, so you do not need to open the file.

For each file that has less than 100% test coverage, find the matching file in cov_annotate and review the file.

If a line starts with a ! (exclamation mark), it means that the line is not covered by tests.
Add tests to cover the missing lines.

Keep running the tests and improving coverage until all lines are covered.

More from github/awesome-copilot

SkillDescription
acquire-codebase-knowledgeUse this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
acreadiness-assessRun the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
acreadiness-generate-instructionsGenerate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
acreadiness-policyHelp the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
ad-campaign-analyzerUse this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like "analyze my ad campaigns", "where am I wasting ad spend", "reallocate my ad budget", "which ads are actually working", or "ROAS analysis". Do not trigger for campaign planning or creative generation without performance data.
add-educational-comments'Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.'
adobe-illustrator-integrationsBuild, debug, package, and document Adobe Illustrator integrations: CEP extension panels, C++ SDK plug-ins (.aip), the coming UXP plugins, MCP servers that drive Illustrator, computer-use automation, and OS scripts (batch, PowerShell, bash, AppleScript) that run ExtendScript in Illustrator. Use when creating or fixing an Illustrator extension or plug-in, choosing between a script, extension, or plug-in, connecting an AI tool to Illustrator, automating Illustrator from the command line, or writing install and usage docs for an Illustrator tool someone will publish. Pairs with adobe-illustrator-scripting for the ExtendScript DOM.
adobe-illustrator-scriptingWrite, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
agent-architectureDesign AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.
agent-governance|