「testing」の検索結果

Use when the user wants to add a new MCP server integration — connect a custom tool, API, or service to the plugin by configuring it in .mcp.json with proper credentials and testing.

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This skill enables Claude to perform comprehensive accessibility audits. It uses the accessibility-test-scanner plugin to identify WCAG 2.1/2.2 compliance issues, validate ARIA attributes, check keyboard navigation, and assess screen reader compatibility. Use this skill when the user requests an accessibility scan, audit, or compliance check, or when terms like "WCAG", "ARIA", "screen reader", "accessibility testing", or "a11y" are mentioned. It provides actionable insights for improving web application accessibility.

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Build WCAG 2.1 AA compliant web applications for University of Sheffield. Covers semantic HTML, ARIA patterns, form accessibility, keyboard navigation, color contrast, alt text, captions, and automated testing. Use when creating websites, web apps, forms, interactive components, or auditing accessibility for WCAG compliance, screen readers, keyboard access, or inclusive design.

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Comprehensive WCAG compliance and accessibility testing covering ARIA, keyboard navigation, screen readers, color contrast, and automated a11y validation.

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How to identify and test for account takeover vulnerabilities in web applications. Use this skill whenever the user mentions account takeover, authentication bypass, password reset attacks, email verification bypass, session hijacking, or any technique to compromise user accounts. This includes testing authorization issues, unicode normalization attacks, reset token reuse, CORS/CSRF/XSS exploitation, cookie manipulation, and OAuth vulnerabilities.

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Provide comprehensive techniques for attacking Microsoft Active Directory environments. Covers reconnaissance, credential harvesting, Kerberos attacks, lateral movement, privilege escalation, and domain dominance for red team operations and penetration testing.

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Web accessibility specialist for WCAG compliance, ARIA implementation, and inclusive design. Use when auditing websites for accessibility issues, implementing WCAG 2.1 AA/AAA standards, testing with screen readers, or ensuring ADA compliance. Expert in semantic HTML, keyboard navigation, and assistive technology compatibility.

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Use when you need to write or review programmatic JDBC with Spring — including JdbcClient (Spring Framework 7+) as the default API, JdbcTemplate only where batch/streaming APIs require JdbcOperations, NamedParameterJdbcTemplate for legacy named-param code, parameterized SQL, RowMapper mapping to records, batch operations, transactions, safe handling of generated keys, DataAccessException handling, read-only transactions, streaming large result sets, and @JdbcTest slice testing. This should trigger for requests such as Review Java code for Spring JDBC (JdbcTemplate, JdbcClient, NamedParameterJdbcTemplate); Apply best practices for Spring JDBC data access in Java code; Detect and fix SQL injection risks in JDBC code; Improve transaction boundaries or exception handling for JDBC operations. Part of cursor-rules-java project

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Production-grade accessibility skill for WCAG 2.2 AA compliance. Covers auditing, remediation, component authoring, and validation workflows. Auto-invoked for UI implementation, a11y fixes, and accessibility testing.

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Build websites and web apps using 21st.dev — the largest marketplace of shadcn/ui-based React Tailwind components with 1400+ components. Use when user mentions "21st.dev", "21st", "ใช้ 21st", "จาก 21st", "build with 21st", "21st component", "21st magic MCP", or references the 21st.dev component registry in any language. Covers building complete sites (landing pages, dashboards, portfolios, e-commerce storefronts, auth pages), browsing and installing individual 21st.dev components into existing projects, and setting up the 21st.dev MCP server. Also trigger when user wants premium UI components specifically from the 21st.dev registry. Do NOT trigger for general web development, standard shadcn/ui, plain Tailwind CSS, React Native, API development, testing, deployment, or any task that doesn't explicitly reference 21st or 21st.dev.

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5-level verification pyramid: static→unit→Playwright E2E (homepage-first, 6bp)→AI visual→post-deploy. 8-check quality gate. Multi-agent testing (functional/security/a11y/performance). Playwright v1.59+ AI agents (Planner/Generator/Healer). WCAG 2.2 AA via axe-core v4.11. Percy+Chromatic visual regression. ADA Title II 2027/2028 deadlines.

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Always use browser-harness for any web interaction: automation, scraping, testing, or site/app work.

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Windows アプリ開発 (WinUI 3 / Windows App SDK) のテスト・パフォーマンス・ アクセシビリティ・スレッドリファレンス。MSTest, xUnit, NUnit, WinAppDriver, Appium, Playwright, WebView2 テスト, WACK 認定, MSIX サイドローディング, CI。 プロファイリング, WPT, WPA, GC, 起動性能, XAML レイアウト/読み込み最適化, ListView/GridView 仮想化, DispatcherQueue。UI Automation, AutomationProperties, automation peers, キーボード操作性, ハイコントラストテーマ, Narrator。 DispatcherQueue, DispatcherQueueTimer, WinRT 非同期パターン, async/await, ConfigureAwait, スレッドプール, STA/MTA, デッドロック回避, バックグラウンドタスク。

Android アプリ開発 (Kotlin) のテストリファレンス。JUnit4, Espresso, テストピラミッド。 テストダブル, Mockito, MockK, Truth, Robolectric, coroutines/Flow/ViewModel テスト。 AndroidJUnit4, ActivityScenario, FragmentScenario, UiAutomator, AndroidTestOrchestrator。 createComposeRule, Compose UI テスト同期。

Use OpenAI Codex CLI as a peer collaboration partner — same standing as Claude, not a one-shot tool. Bring Codex in EARLY (ideation, framing decisions, stuck moments), not only for late peer-review. Hand it strategic context (target, research, draft, reasoning), iterate conversationally over ~3-8 turns, converge before surfacing to the human. Invoke when the user says "check / iterate / ideate with Codex" or "ask Codex" — and proactively when stuck between two reasonable framings, pressure-testing a load-bearing claim or structure, or about to ask the human to pick between options two agents could resolve.

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Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

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Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.

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Analyze spatial cell-cell interactions, neighborhoods, and niches in IMC/MIBI data with squidpy and imcRtools, covering neighborhood-enrichment permutation nulls, the abundance-vs-density confound, inhomogeneous Ripley's K, cellular-neighborhood discovery, graph-construction (contact vs proximity), and edge effects. Use when testing whether cell types co-locate, choosing a spatial null, building a neighbor graph, discovering tissue niches, or deciding whether a spatial pattern is real or a density/segmentation artifact.

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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.

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Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.

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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.

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Performs differential expression on bulk RNA-seq count data with DESeq2's negative-binomial GLM, Wald and LRT testing, apeglm/ashr/normal LFC shrinkage, independent filtering, Cook's outlier handling, VST/rlog transforms, and design formulas including paired, batch, and interaction terms. Use when running bulk DE, choosing DESeq2 over edgeR or limma-voom, building a paired or interaction design, applying LFC shrinkage for ranking or GSEA, choosing Wald vs LRT, troubleshooting padj=NA, picking VST vs rlog, importing salmon/kallisto via tximport, or analyzing prokaryotic RNA-seq.

日本語の概要は準備中です。原文の説明を表示しています。