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documentation testing AI Skills

Discover documentation testing skills, compare source repositories and read the instructions for each workflow.

Categories: documentation testing

Showing 649–672 of 4,461

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GitHub

mutation-testing

aj-geddes

Evaluate test suite quality by introducing code mutations and verifying tests catch them. Use for mutation testing, test quality, mutant detection, Stryker, PITest, and test effectiveness analysis.

GitHub

visual-regression-testing

aj-geddes

Detect unintended visual changes in UI by comparing screenshots across versions. Use for visual regression, screenshot diff, Percy, Chromatic, UI testing, and visual validation.

GitHub

synthetic-monitoring

aj-geddes

Implement synthetic monitoring and automated testing to simulate user behavior and detect issues before users. Use when creating end-to-end test scenarios, monitoring API flows, or validating user workflows.

GitHub

code-review-analysis

aj-geddes

Perform comprehensive code reviews with best practices, security checks, and constructive feedback. Use when reviewing pull requests, analyzing code quality, checking for security vulnerabilities, or providing...

GitHub

test-data-generation

aj-geddes

Generate realistic, consistent test data using factories, fixtures, and fake data libraries. Use for test data, fixtures, mock data, faker, test builders, and seed data generation.

GitHub

test-automation-framework

aj-geddes

Design and implement scalable test automation frameworks with Page Object Model, fixtures, and reporting. Use for test framework, page object pattern, test architecture, test organization, and automation infras...

GitHub

mobile-app-testing

aj-geddes

Comprehensive mobile app testing strategies for iOS and Android. Covers unit tests, UI tests, integration tests, performance testing, and test automation with Detox, Appium, and XCTest.

GitHub

cypress-e2e-testing

influxdata

Run, validate, and analyze Cypress E2E tests for the InfluxData documentation site. Covers Hugo server management, test execution modes, and failure analysis.

GitHub

dotnet-test

NikiforovAll

This skill should be used when running .NET tests selectively with a build-first, test-targeted workflow. Use it for running tests with xUnit focus.

GitHub

mutation-test

jmagly

Run mutation testing to validate test quality beyond code coverage. Use when assessing test effectiveness, finding weak tests, or validating test suite quality.

GitHub

flaky-fix

jmagly

Suggest and apply fixes for flaky tests based on detected patterns. Use after flaky-detect identifies unreliable tests that need repair.

GitHub

flaky-detect

jmagly

Identify flaky tests from CI history and test execution patterns. Use when debugging intermittent test failures, auditing test reliability, or improving CI stability.

GitHub

vitest-runner

jmagly

Execute JavaScript/TypeScript tests with Vitest, supporting coverage, watch mode, and parallel execution. Use for JS/TS test automation.

GitHub

flaky-detect

jmagly

Identify flaky tests from CI history and test execution patterns. Use when debugging intermittent test failures, auditing test reliability, or improving CI stability.

GitHub

flaky-fix

jmagly

Suggest and apply fixes for flaky tests based on detected patterns. Use after flaky-detect identifies unreliable tests that need repair. Use when relevant to the task.

GitHub

generate-factory

jmagly

Auto-generate test data factories from schemas, types, or models. Use when creating test data infrastructure, setting up fixtures, or reducing test setup boilerplate.

GitHub

mutation-test

jmagly

Run mutation testing to validate test quality beyond code coverage. Use when assessing test effectiveness, finding weak tests, or validating test suite quality.

GitHub

pytest-runner

jmagly

Execute Python tests with pytest, supporting fixtures, markers, coverage, and parallel execution. Use for Python test automation.

GitHub

test-sync

jmagly

Detect orphaned tests, obsolete assertions, and test-code misalignment. Use for test suite maintenance, cleanup, and traceability validation.

GitHub

test-coverage

jmagly

Analyze test coverage, identify gaps, and recommend test improvements. Use when relevant to the task.

GitHub

generate-factory

jmagly

Auto-generate test data factories from schemas, types, or models. Use when creating test data infrastructure, setting up fixtures, or reducing test setup boilerplate.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.

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