Testing Claude Skills
3 546 testing skills from 425 authors. They run test suites, check accessibility and catch what broke. Half of them fit into 1 796 tokens or less — that is what one costs your context window when the agent loads it. 405 ship runnable scripts rather than instructions alone. 2 of them cannot work without an MCP server, most often rube. We also found 403 copies of these same skills sitting in other people's repositories — counted once here, not 403 times.
3 546 unique 425 authors 2 161 updated this month 331 from vendors
Conduct comprehensive SSH security assessments including enumeration, credential attacks, vulnerability exploitation, tunneling techniques, and post-exploitation activities. This skill covers the complete methodology for testing SSH service security.
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Master TDD orchestrator specializing in red-green-refactor discipline, multi-agent workflow coordination, and comprehensive test-driven development practices.
Test-Driven Development workflow principles. RED-GREEN-REFACTOR cycle.
Use when working with tdd workflows tdd cycle
Implement the minimal code needed to make failing tests pass in the TDD green phase.
Generate failing tests for the TDD red phase to define expected behavior and edge cases.
Use when working with tdd workflows tdd refactor
Use when working with tdd workflows tdd cycle (Alias for tdd-workflows-tdd-cycle)
Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.
Comprehensive testing approaches for Temporal workflows using pytest, progressive disclosure resources for specific testing scenarios.
Master AI-powered test automation with modern frameworks, self-healing tests, and comprehensive quality engineering. Build scalable testing strategies with advanced CI/CD integration.
Systematically identify and fix all failing tests using smart grouping strategies. Use when explicitly asks to fix tests (\"fix these tests\", \"make tests pass\"), reports test failures (\"tests are failing\", \"test suite is broken\"), or completes implementation and wants tests passing.
Review generated or changed test code against universal testing rules before it ships or is presented for approval.
Jest testing patterns, factory functions, mocking strategies, and TDD workflow. Use when writing unit tests, creating test factories, or following TDD red-green-refactor cycle.
Comprehensive testing and QA workflow covering unit testing, integration testing, E2E testing, browser automation, and quality assurance.
Rigorous visual validation expert specializing in UI testing, design system compliance, and accessibility verification.
Generate comprehensive, maintainable unit tests across languages with strong coverage and edge case focus.
13-phase production QA for fullstack Next.js apps: build verification, SEO tags, OG images, favicon, route regression, API auth, page speed, lazy load, vulnerability scan, UI/UX cards, error boundaries, database, secure rendering, and cleanup.
Generates Vitest tests in JavaScript/TypeScript with Vite-native speed. Jest-compatible API with ESM support and HMR. Use when user mentions "Vitest", "vi.mock", "vitest.config". Triggers on: "Vitest", "vi.mock", "vi.fn", "Vite test", "vitest config".
Web application security testing workflow for OWASP Top 10 vulnerabilities including injection, XSS, authentication flaws, and access control issues.
Master comprehensive testing strategies for smart contracts using Hardhat, Foundry, and advanced testing patterns.
To test local web applications, write native Python Playwright scripts.
Answer repository questions grounded entirely in source code evidence. Use when user asks a question about the codebase, user wants to understand a specific file, function, or component, or user asks \"how does X work\" or \"where is Y defined\".
Provide systematic methodologies for discovering and exploiting privilege escalation vulnerabilities on Windows systems during penetration testing engagements.
PLAN and GENERATE WJTTC (Championship-Grade) test suites for any project. Analyzes the codebase, classifies components across the WJTTC five tiers (Brake · Engine · Aero · Tyre · Pit), writes a tiered test plan, and scaffolds executable test files. This is the BUILDER — it plans and...
F1-inspired test EXECUTOR + reporter. Runs a test plan, finds and reproduces bugs, audits suite signal integrity, then files a WJTTC report (Brake/Engine/Aero/Tyre/Pit) with a tier verdict. Use when you need to test code, validate functionality, reproduce a failure, or produce a test...
Assess WordPress installations for common vulnerabilities and WordPress 7.0 attack surfaces.
Use this skill when implementing tasks according to Conductor's TDD workflow, handling phase checkpoints, managing git commits for tasks, or understanding the verification protocol.
Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
Configure and test common network services (HTTP, HTTPS, SNMP, SMB) for penetration testing lab environments. Enable hands-on practice with service enumeration, log analysis, and security testing against properly configured target systems.
Provide comprehensive techniques for escalating privileges from a low-privileged user to root/administrator access on compromised Linux and Windows systems. Essential for penetration testing post-exploitation phase and red team operations.
Provide systematic methodologies for leveraging Shodan as a reconnaissance tool during penetration testing engagements.
Execute comprehensive SQL injection vulnerability assessments on web applications to identify database security flaws, demonstrate exploitation techniques, and validate input sanitization mechanisms.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Use whenever the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in".
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.
Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.
Run structured What-If scenario analysis with 4–6 branch possibility exploration (best, likely, worst, wild card, contrarian, second-order). Use when the user asks speculative what-if questions about uncertain futures, strategic forks, contingency planning, or stress-testing a decision before committing.
Apply Grounded Theory (Glaser and Strauss) to build theory inductively from qualitative data through open, axial, and selective coding. Use this skill when the user needs to develop new theory from data rather than test existing hypotheses, conduct theoretical sampling and constant comparison, determine when theoretical saturation is reached, or when they ask 'what theory explains this phenomenon', 'how do I code qualitative data systematically', or 'when do I stop collecting data'.
Apply Structural Equation Modeling (SEM) to test hypothesized causal structures by combining measurement models (CFA) and structural models (path analysis). Use this skill when the user needs to validate latent constructs, test mediation or moderation paths, assess model fit with CFI/TLI/RMSEA/SRMR, or when they ask 'do these variables form a causal chain', 'how do I test my theoretical model', or 'is my measurement model valid'.
Apply Socratic questioning — systematic inquiry via clarification, assumption-probing, evidence-testing, perspective-shifting, implication-tracing, and meta-questions — to coach learning or surface hidden assumptions in a person's reasoning. Use this skill when the user is explicitly facilitating learning, coaching a person through their own thinking, or needs a structured questioning sequence to probe a held belief, even if they say 'play devil's advocate on this claim' or 'how should I coach my team through this problem via questions'. Do NOT use for open-ended brainstorming, information gathering, or requirements-discovery question lists where no belief is being probed.
Apply systems thinking — causal loop diagrams, stock-and-flow models, system archetypes, and leverage-point analysis — to organizational, economic, or social problems where feedback loops, delays, or emergent behavior drive recurring failure across multiple interacting actors. Use this skill when the user describes a multi-actor situation that resists linear fixes: policy interventions that backfire, org-level fixes that break other teams, market symptoms that return after being solved, or time-lagged second-order consequences, even if they say 'why does fixing X make Y worse' or 'identify the leverage points in this system'. Do NOT use for single-cause software bugs, flaky tests, or regressions — those are debugging problems, not systems-thinking problems, even when phrased as 'this keeps coming back'.
Optimize digital advertising campaigns across Google Ads, Meta Ads, and LINE LAP including bidding strategies, audience targeting, creative testing, and ROAS optimization. Use this skill when the user needs to improve ad performance, reduce CPA, select bidding strategies, or allocate budget across platforms — even if they say 'our ads aren't working', 'reduce our cost per acquisition', 'Google vs Facebook ads', or 'improve our ROAS'.
Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: \"Why is X failing?\", \"Where does this error come from?\", \"Trace this bug\