Testing Claude Skills
3 543 testing skills from 425 authors. They run test suites, check accessibility and catch what broke. Half of them fit into 1 799 tokens or less — that is what one costs your context window when the agent loads it. 404 ship runnable scripts rather than instructions alone. 2 of them cannot work without an MCP server, most often rube. We also found 429 copies of these same skills sitting in other people's repositories — counted once here, not 429 times.
3 543 unique 425 authors 2 updated this month 331 from vendors
| Compile a LaTeX project and run basic QA (missing refs, bib errors, broken citations), producing `latex/main.pdf` and a build report.
Set up or run design QA gates — token + hardcoded-value lint, automated a11y (axe), contrast, visual regression across variants/states/themes/RTL, and the manual a11y checklist. Use when the user wants CI quality gates, to prevent design regressions, or to QA a component/screen before shipping.
Move an idea up the fidelity ladder (content-first → wireframe → low-fi → high-fi → code) with a validation plan at each level, plus user-journey mapping and usability-testing scripts. Use when the user wants to prototype, wireframe, map a user flow, or plan/run usability testing.
| Solutioning flagship — shards a PRD + architecture into epics.md and individual {epic}.{story}.{slug}.story.md context objects, the LAST planning artifact before Dev Notes with SOURCE CITATIONS back to prd.md/architecture.md, Acceptance Criteria, Tasks/Subtasks mapped to ACs, Testing strategy, Dependency Maps, an explicit Owned File/Module Scope list (the lever for parallel-conflict-free scheduling), and Learnings from Previous Stories. Sized to one dev-day; split if larger; NO story points. Use when the user says "shard the PRD", "create epics", "break the PRD into epics", "break this epic into stories", "create stories", "create a story", "draft story files", "generate the story for X", "prepare stories for dev", "mark the story ready for dev", epics/stories, Update an existing story, or Validate a draft against the contract.
Use this skill to ensure Jetpack Compose performance numbers reflect production reality by measuring against a release variant with R8 enabled, Live Literals disabled, and Compose Compiler reports read from the release output directory. Covers why debug builds lie (interpreted Compose runtime, JIT warmup, Live Literals constant-getters), how to set up a release-with-symbols measurement build, and how to wire Macrobenchmark, Compose Compiler reports, Layout Inspector, simpleperf, and Android Studio Profiler against it. Cited result is roughly 75 percent startup gain and 60 percent frame-render gain debug to release. Use when the developer reports "slow startup", "jank", "dropped frames", "high recomposition count", or quotes timings from `assembleDebug`, Layout Inspector, or `CompilationMode.None`. Use when reviewing a perf bug, setting up a CI perf gate, or before filing a perf regression.
Use this skill to find which Jetpack Compose composables are recomposing and why, using Android Studio Layout Inspector recomposition counts and skip counts, the per-parameter Argument Change Reasons (Changed / Unchanged / Uncertain / Static / Unknown) introduced in Android Studio Hedgehog and later, and runtime `@TraceRecomposition` from `compose-stability-analyzer` for production-like measurement. Walks through enabling counts, mapping each Argument Change Reason to a fix, and confirming the result in a release build. Use when the developer says "this should be skipping but isn't", "I want to see recomposition counts", asks what "Uncertain" or "Unknown" means in the inspector, or needs to confirm a stability or strong-skipping fix actually worked end-to-end.
Use this skill to set up a CI gate that fails the build when Compose stability silently regresses, using the `skydoves/compose-stability-analyzer` Gradle plugin (primary) or the `j-roskopf/ComposeGuard` plugin (alternative for non-Android multiplatform). Covers applying `com.github.skydoves.compose.stability.analyzer`, configuring the `composeStabilityAnalyzer { stabilityValidation { ... } }` DSL, generating a `.stability` baseline with `:stabilityDump`, committing it to version control, and wiring `:stabilityCheck` into a GitHub Actions or other CI job. Use when the team wants a stability SLO, when an unstable parameter slipped into a shared data class and went unnoticed until jank was reported, when migrating a large app to strong skipping, or when the user mentions stabilityCheck, stabilityDump, baseline drift, ComposeGuard, or "fail the build on stability regression".
> Generate a Datadog Terraform provider data source end to end and open a review-ready (operationIds), artifact name, cardinality, description, and overwrite target; (2) Generation — build an annotated OpenAPI slice with slice_and_annotate.py, run tfgen on it, run make docs/build, and commit onto a new branch; (3) PR — a quick runtime-risk scan (trusting the generator for correctness), draft the standard PR body with disclaimers + testing guide, and open the PR with `gh`. Use this skill whenever the user wants to generate a Datadog data source, mentions tfgen / generator-v2, slice_and_annotate, an OpenAPI operation they want a data source for, opening a PR for a generated data source, evaluating generated code against goldens, or writing cassette / acceptance-test instructions — even if they don't say "skill".
> End-to-end workflow to diagnose, reproduce, fix, and validate a failing Datadog Terraform provider integration test. Takes any input pointing at other description of what's failing. Runs autonomously through 8 phases — identify → validate in CI → reproduce locally → plan fix → execute → open draft PR → monitor → report.
Run Datadog Terraform provider acceptance tests with proper RECORD modes and capture output for analysis. This agent is invoked via Task tool and writes results to files.
Review test code for quality, design, and completeness after implementing a feature or fixing a bug. Use when the user asks to "review my tests", "check my test quality", "are these tests good enough", "review testing", or after completing a feature implementation that includes tests. Also use when tests feel brittle, flaky, or superficial. Cross-references production code to find coverage gaps.
Best practices for writing R package tests using testthat version 3+. Use when writing, organizing, or improving tests for R packages. Covers test structure, expectations, fixtures, snapshots, mocking, and modern testthat 3 patterns including self-sufficient tests, proper cleanup with withr, and snapshot testing.
R package development with devtools, testthat, and roxygen2. Use when the user is working on an R package, running tests, writing documentation, or building package infrastructure.
> Form optimization for lead capture, contact, demo request, application, and checkout forms. Covers field-cost analysis, multi-step form design, validation UX, mobile optimization, and A/B testing frameworks.
> Popup and modal optimization for conversion. Covers exit-intent, slide-ins, banners, timing optimization, frequency capping, audience targeting, compliance, and A/B testing frameworks for lead capture, promotions, and announcements.
> Plan, sequence, and pressure-test internal announcements before they send. Use when announcing a reorg, policy, or product change, drafting an all-hands or exec update, or deciding who hears what and in which order.
> Headcount and delivery-capacity planning — effective capacity from raw headcount, hire/contract/defer scenarios, and capacity-vs-commitment gap reports. Use when planning a quarter, sizing a hiring ask, or testing whether a roadmap fits.
> Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.
> Test and evaluation harness for AI agents — scenario suites, deterministic replay, regression diffing, cost and latency budgets. Use when agent quality is vibe-checked, before shipping a prompt or model change, or when evals drift.
> contract, k6 load testing, mocking, and OpenAPI-driven generation. Use when adding new APIs, auditing test coverage, or building regression suites.
> chain-of-thought, few-shot, regression testing, and rubrics. Use when designing production prompts, running A/B tests, or building prompt libraries.
> bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow endpoints, memory growth, large bundles, or traffic spikes.
> test diagnosis, visual regression, and CI integration. Use when writing E2E tests, fixing flaky tests, or migrating from Cypress/Selenium.
> Browser-based QA combining Chrome MCP control with Python analysis tools. Use when performing browser QA testing, visual regression tracking, WCAG accessibility auditing, performance profiling, or health-scoring web applications.
> Testing for React/Next.js with Jest, React Testing Library, and Playwright. Use when generating tests, writing unit tests, analyzing coverage, scaffolding E2E tests, configuring Jest, or improving test quality.
> Validate and score Claude Code skill packages for quality, completeness, and best-practice compliance. Tests Python scripts, checks YAML frontmatter, and generates reports. Use when creating, validating, or auditing skill packages.
Sample skill for testing the skill-tester validation pipeline. Demonstrates proper skill structure with scripts, references, and assets.
> based billing, idempotent webhooks, customer portal, dunning, and SCA. Use when building billing, handling webhooks, or testing with Stripe CLI.
> Guide red-green-refactor TDD with test generation, coverage-gap analysis, and multi- framework support. Use when writing tests first, analyzing coverage, generating test stubs, or converting tests between Jest, Pytest, JUnit, and Vitest.
> significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
> Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
> Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
This skill should be used when writing test cases, fixing bugs, analyzing code for potential issues, or improving test coverage for JavaScript/TypeScript applications. Use this for unit tests, integration tests, end-to-end tests, debugging runtime errors, logic bugs, performance issues, security vulnerabilities, and systematic code analysis.
This skill should be used when the user asks to "verify a fix", "reproduce failure", "diagnose issue", "check BEFORE/AFTER state", "VF task", "reality check", "check test quality", "mock-only tests", or needs guidance on verifying fixes by reproducing failures before and after implementation, or detecting mock-heavy test anti-patterns.
Headless root-cause debugging loop for a Foreman worker whose tests, build, or acceptance check are failing — especially on a retry. Find the root cause before changing anything, fix at the source with a regression test, and never thrash on symptom patches. Used inside a foreman-tdd build session; emits no summary of its own.
Headless implementation-plan authoring for the Foreman planning stage. Explore the target repo first, then write a deep, decomposition-aware plan that the grill→ADR/PRD→issues pipeline can build on — goals, seams, data/interface changes, risks, sequencing, and testing strategy. No placeholders. Writes the plan body and stops.
Headless end-to-end / web-app testing for the Foreman e2e stage. Derive end-to-end tests from the PRD's User Flows, drive the real application the way a user would (browser flows, screenshots, accessibility checks for web apps), make them pass via the configured e2e command, and emit the FOREMAN-SUMMARY block Foreman parses.
Stack-agnostic test-driven development loop for a single Foreman issue. Implements one vertical slice with strict red-green-refactor (one test at a time, never horizontal slicing) using the foreman-test wrapper, saves completion evidence, then emits a machine-readable FOREMAN-SUMMARY block Foreman parses.
Headless self-verification gate a Foreman worker runs before it claims an issue is done. Re-run the real commands, read the actual output, and only then write the FOREMAN-SUMMARY — evidence before claims, always. Used inside a foreman-tdd build session; emits no summary of its own.
Use when Java implementation work should be guided by Test-Driven Development, including maintaining a test list, choosing the next behavior, writing a failing test first, implementing only enough production code to pass, and refactoring while keeping tests green. This should trigger for requests such as Apply TDD; Use test-driven development; Drive this Java change with tests; Write the failing test first; Red-green-refactor this feature. Part of Plinth Toolkit
Use when designing, implementing, reviewing, testing, or cleaning up feature toggles, feature flags, kill switches, runtime configuration gates, canary controls, staged rollouts, experiments, or temporary compatibility switches in Java enterprise systems. This should trigger for requests such as Design a feature toggle strategy; Review this feature flag; Add a kill switch safely; Test toggle on and off paths; Clean up an expired feature toggle; Plan controlled rollout and rollback behavior. Part of Plinth Toolkit
Use when you need to add or evaluate Maven dependencies that improve code quality or domain modeling — including nullness annotations (JSpecify), static analysis (Error Prone + NullAway), functional programming (VAVR), architecture testing (ArchUnit), or money and currency support (JavaMoney) — and want a consultative, question-driven approach that adds only what you actually need. This should trigger for requests such as Add Maven dependencies; Add JSpecify nullness dependencies; Add Error Prone NullAway dependencies; Add VAVR functional dependencies; Add ArchUnit architecture testing dependencies; Add JavaMoney dependencies. Part of Plinth Toolkit
Use when you need to apply testing strategies for Java code — RIGHT-BICEP to guide test creation, A-TRIP for test quality characteristics, or CORRECT for verifying boundary conditions. This should trigger for requests such as Review Java code for testing strategies; Apply RIGHT-BICEP testing strategies in Java code; Apply A-TRIP testing strategies in Java code; Apply CORRECT boundary condition verification in Java code. Part of Plinth Toolkit
Use when you need to review, improve, or write Java unit tests — including migrating from JUnit 4 to JUnit 5, adopting AssertJ for fluent assertions, structuring tests with Given-When-Then, ensuring test independence, applying parameterized tests, mocking dependencies with Mockito, verifying boundary conditions (RIGHT-BICEP, CORRECT, A-TRIP), leveraging JSpecify null-safety annotations, or eliminating testing anti-patterns such as reflection-based tests or shared mutable state. This should trigger for requests such as Review Java code for unit tests; Apply best practices for unit tests in Java code; Write fast JUnit unit tests for Java code; Improve Mockito-based Java unit tests; Refactor Java tests to isolate collaborators. Part of Plinth Toolkit
Use when you need to set up, review, or improve Java integration tests — including generating a BaseIntegrationTest.java with WireMock for HTTP stubs, detecting HTTP client infrastructure from import signals, injecting service coordinates dynamically via System.setProperty(), creating WireMock JSON mapping files with bodyFileName, isolating stubs per test method, verifying HTTP interactions, or eliminating anti-patterns such as Mockito-mocked HTTP clients or globally registered WireMock stubs. This should trigger for requests such as Review Java code for integration tests; Apply best practices for integration tests in Java code; Write Java integration tests with real infrastructure boundaries; Improve Testcontainers integration tests for Java code; Review integration test setup and teardown in Java projects. Part of Plinth Toolkit
Use when you need to implement acceptance tests from maintainer-sanitized Gherkin scenario facts for framework-agnostic Java (no Spring Boot, Quarkus, Micronaut) — confirming @acceptance scenarios before coding, happy path with RestAssured, DB/Kafka test fixtures, WireMock for external REST only, and *AT classes run by Failsafe. This should trigger for requests such as Review Java code for acceptance tests; Apply best practices for acceptance tests in Java code; Implement acceptance tests from Gherkin scenarios in Java; Map feature files to Java step definitions; Review Cucumber acceptance tests for Java services. Part of Plinth Toolkit
Use when you need to set up Gatling performance testing for a Java Maven project — including adding Gatling dependencies and the Gatling Maven plugin, creating Java simulations, running gatling:test, configuring a simulation class, and reviewing generated reports. This should trigger for requests such as Add Gatling performance testing; Apply Gatling performance testing; Create a Gatling simulation; Add Gatling support; Review Gatling performance test assertions and feeders. Part of Plinth Toolkit
Use when you need to set up JMeter performance testing for a Java project — including creating the run-jmeter.sh script from the exact template, configuring load tests with loops, threads, and ramp-up, or running performance tests from the project root with custom or default settings. This should trigger for requests such as Improve the code with JMeter performance testing; Apply JMeter performance testing; Refactor the code with JMeter performance testing; Add JMeter support; Create a JMeter test plan for a Java service. Part of Plinth Toolkit