「diagnostics」の検索結果

Guidance for Tauri v2 os-info plugin with safe system diagnostics and reporting.

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。

Route non-critical nets on a real KiCad PCB or Specctra DSN through Circuit Weaver's fail-closed Freerouting integration. Use after placement and net-class setup when the user requests autorouting, a validated SES session, routing diagnostics, or post-route cleanup. Keep critical power, RF, clock, crystal, switching, and differential nets manual.

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。

Build and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta, remote egress requires explicit consent, and unavailable reranking falls back deterministically.

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。

Audit and score blog posts on a 5-category 100-point scoring system covering content quality, SEO optimization, E-E-A-T signals, technical elements, and AI citation readiness. Includes advisory editorial style diagnostics (sentence-length variation, configured phrase lists, vocabulary sampling) that never infer authorship or affect scoring. Supports export formats (markdown, JSON, table) and batch analysis with sorting. Generates prioritized recommendations (Critical/High/Medium/Low) with specific fixes. Works with any format (MDX, markdown, HTML, URL). Use when user says "analyze blog", "audit blog", "blog score", "check blog quality", "blog review", "rate this blog", "blog health check".

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。

Run a source-grounded paid-advertising audit for one or more of Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for full ad checks, account health reviews, paid-media diagnostics, partial audits after authentication or worker failure, missing-platform weighting, beta-feature eligibility and scoring, spend audits, tracking audits, or prioritized opportunities and risks.

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。

Diagnoses Google Ads account performance issues such as conversion loss (value or volume), low lead flow/volume, and lost impression share (opportunities) due to ad rank, bids, or budgets. Use when troubleshooting sudden performance drops, analyzing campaign impression share metrics, investigating low lead flow, or searching for bidding and budget constraints. Don't use for setting up new campaigns, uploading conversion events directly, or general Google Mobile Ads SDK integration issues (use gma-android-integrate instead).

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。

Distinguish the six real reasons a Salesforce query can 'fail', and the protocol for diagnosing before declaring: object doesn't exist, not queryable in edition, permission-denied, field-level errors, namespace prefix missing, API version mismatch. NOT for one object's own query restrictions, required filters or row caps - use apex/soql-object-limits-and-restrictions. NOT for a query that works but is slow - use data/soql-query-optimization.

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。

Use when a Lightning record page, home page, or app page is slow to load or render — covers Experienced Page Time (EPT) analysis, component count reduction, progressive disclosure via tabs and conditional rendering, Lightning Experience Insights diagnostics, and DOM/XHR minimization strategies. Triggers: 'Lightning page is slow', 'EPT is high', 'record page takes too long to load', 'too many components on page', 'Lightning Experience Insights shows slow page', 'how to optimize Lightning page performance'. NOT for Visualforce page performance (separate concern). NOT for Apex or SOQL query optimization (use apex/apex-cpu-and-heap-optimization or data/soql-query-optimization). NOT for report or dashboard performance (use admin/report-performance-tuning).

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。

Use when Codex needs language-server diagnostics, definitions, references, symbols, or rename safety checks in the current workspace.

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。

Use this skill when creating, scaffolding, reviewing, or maintaining Python Home Assistant custom integrations, including work on custom_components, manifests, config flows, options flows, config entries, platforms/entities, DataUpdateCoordinator, integration automation triggers, diagnostics, repairs, discovery/networking, authentication, HACS-ready repositories, or Home Assistant integration quality scale compliance.

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。

Use when morphir-scala work needs environment diagnostics or build/network/sandbox unblocking; reference repository management; branch lifecycle refresh after squash-merging a target branch into main; Morphir IR spec/schema sync or export; or task-tracking configuration and beads mode resolution.

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。

This skill should be used when the user asks to "implement a DiD regression", "run a staggered difference-in-differences", "set up an event study", "implement IV / 2SLS", "run a regression discontinuity design", "build synthetic control", "do propensity score matching", "test parallel trends", "run Honest DiD", "wild cluster bootstrap", "Callaway-Sant'Anna", "Sun-Abraham", "Bacon decomposition", "double machine learning", "debiased machine learning", "DML / DoubleML", "cross-fitting", "causal forest", "heterogeneous treatment effects", "ATE with ML controls", "which causal design fits my data", or needs causal-inference code, diagnostics, or identification writing in Python (StatsPAI), R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape, with runnable validated templates, ML-based causal inference patterns, and Journal of Finance applications.

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。

Add or review dygo Logs, Activity, Audit Logs, health checks, diagnostics, metrics, traces, and operator-visible failure states. Use when runtime behavior must become inspectable or diagnosable.

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。

Query and filter Datadog logs from the shell using the Composio CLI. Run scoped log searches, pivot across services/environments, and export structured JSON for incident investigation and diagnostics.

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。

Diagnose Azure application and infrastructure issues with a structured, evidence-based workflow. Use this skill when users ask to troubleshoot outages, latency, failures, configuration drift, or anomalies. Root cause analysis using logs, metrics, traces, and dependency mapping.

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。

Build and diagnose visionOS projects with Seiro MCP. Use inspect_xcode_schemes (if needed) -> validate_sandbox_policy -> build_visionos_app -> inspect_build_diagnostics (on failure) -> fetch_build_output (on success), and avoid direct xcodebuild or swiftc unless the user explicitly asks for shell-level execution or MCP cannot handle the task.

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。

Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.

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。

Build forest plots (HR, OR, RR, beta-coefficient summaries with CIs) and funnel plots (meta-analysis publication-bias diagnostics) using forestplot, metafor, ggforest, and MendelianRandomization with proper axis-scaling, summary-diamond placement, subgroup nesting, and Egger / trim-and-fill asymmetry tests. Use when summarizing effects across subgroups, trials, or instruments — meta-analysis, Mendelian randomization, subgroup HRs.

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。

Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.

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。

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.

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。

Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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。

Build Manhattan, Miami, QQ, and locuszoom-style regional plots from GWAS, TWAS, PWAS, and QTL summary statistics with correct genomic-inflation diagnostics, multi-trait overlays, lead-SNP labeling, and LD-aware regional rendering. Use when visualizing association results across the genome, comparing two traits, computing genomic inflation lambda, or zooming into a locus with LD coloring.

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。

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.

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。

Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.

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。