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Choose and optionally run .NET test commands that match the project's SDK, test platform, and framework. Target specific tests, collect reports or diagnostics, and report execution results using syntax appropriate to the repository.
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Assemble a minimal redacted Attio diagnostic bundle with request fingerprints, schema context, retry evidence, and environment metadata. Use when escalating an Attio integration failure without leaking CRM data or secrets. Trigger with "Attio debug bundle", "Attio support evidence", or "collect Attio diagnostics".
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Collect a deterministic, secret-safe Alchemy diagnostic bundle for triage and escalation. Use when a provider issue needs reproducible evidence. Trigger with "Alchemy debug bundle", "collect Alchemy diagnostics", or "prepare an Alchemy support case".
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Collect a bounded, secret-safe Algolia diagnostic bundle for an incident or support escalation. Use when a reproducible search or indexing failure needs portable evidence. Trigger with "Algolia debug bundle", "collect Algolia diagnostics", or "Algolia support evidence".
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Analyzes clinical diagnostic accuracy studies for bias using the QUADAS-2 tool. Use when Claude needs to assess the quality, risk of bias, or applicability of diagnostic accuracy studies (e.g., "Assess this paper using QUADAS-2").
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Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.
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Kubernetes diagnostics for metrics, health checks, resource comparisons, and cluster analysis. Use when analyzing cluster health, comparing environments, or gathering diagnostic data.
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Bevy ECS skill for logs and diagnostics. Keywords: Bevy, LogPlugin, DiagnosticsPlugin, FrameTimeDiagnosticsPlugin.
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Bevy ECS skill for logs and diagnostics. Keywords: Bevy, LogPlugin, DiagnosticsPlugin, FrameTimeDiagnosticsPlugin.
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Add a new diagnostic code following the defineDiagnostics() conventions from nostics
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Use when a task needs the judgment of a Diagnostic Medical Sonographer — deciding whether a duplex, compression, or graded-compression ultrasound study is diagnostic versus technically limited, choosing when to interrupt a scanning protocol to escalate a real-time critical finding, setting scan technique and ALARA (TI/MI) parameters for a given exam type, or drafting the technical worksheet and preliminary findings a physician will interpret and sign.
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Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.
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Comprehensive regression model diagnostics and assumption checking. Use when validating regression models, checking assumptions (linearity, homoskedasticity, normality, independence), detecting outliers/influence, testing multicollinearity, or when user mentions residuals, heteroskedasticity, VIF, Cook's distance, or diagnostic plots.
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Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.
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ネットワーク品質を総合的に診断し、ボトルネックの特定と根本原因の深堀りまで行うスキル。 接続情報、レイテンシ、ダウンロード速度、HTTP接続タイミング、経路解析を自動実行し、 品質閾値に基づく総合評価を日本語レポートとして出力する。 外部依存なし（OS標準ツールのみ使用）、macOSおよびLinux対応。 Use when diagnosing network quality, troubleshooting slow connections, identifying network bottlenecks, measuring latency/bandwidth/jitter, or generating network health reports. 「ネットワーク診断」「network diagnostics」「ネットワークが遅い」 「latency check」「bandwidth test」「traceroute analysis」
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Collect and analyze on-device performance metrics and crash diagnostics using MetricKit. Use when setting up MXMetricManager, handling MXMetricPayload or MXDiagnosticPayload, processing crash/hang/disk-write diagnostics via MXCallStackTree, adding custom signpost metrics, or uploading telemetry to an analytics backend.
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iOS プロジェクトのエラー・警告を XcodeBuildMCP 経由で診断する。「check」「errors」「warnings」「diagnostics」「issues」「compile check」「lint」「エラーチェック」「コンパイルチェック」「警告チェック」などのキーワードで自動適用。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Complete guide for xUnit test output and logging. Use when you need to implement test output, diagnostic logging, or ILogger alternatives in xUnit tests. Covers ITestOutputHelper injection, AbstractLogger pattern, structured output design. Includes XUnitLogger, CompositeLogger, performance test diagnostic tool implementations. Keywords: ITestOutputHelper, ILogger testing, test output xunit, test output, test logging, AbstractLogger, XUnitLogger, CompositeLogger, testOutputHelper.WriteLine, test diagnostics, logger mock, test log, structured output, Received().Log
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개발 전에 제품의 목적을 검토하고 추진 여부, 첫 사용의 장애물, 기능 우선순위를 정리합니다. 근거와 다음 행동을 담은 기획서나 로드맵을 작성합니다.
네트워크 간 경로를 교환하는 BGP의 연결 장애와 경로 누락을 조사합니다. 설정을 바꾸지 않고 연결 상태, 도달 가능성, 경로 제어 설정에서 진단 자료를 수집합니다.