「dj」の検索結果

Convert Next.js bundle analyzer data to NDJSON and explore it

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 deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.

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。

Play and mix audio in Godot 4.7: AudioStreamPlayer (2D/3D variants), audio buses with volume/mute and effects, music vs SFX routing, db/linear volume, and precise sync-to-beat playback timing. Use when playing sounds or music in a Godot project, routing AudioStreamPlayer nodes to buses, adjusting bus volume via AudioServer, or syncing gameplay to the beat.

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 a comparative market analysis (CMA) to price a property. Use when asked to do a CMA, a comparative market analysis, price a home, or estimate a property's value from comparables. Produces a structured CMA — the subject property, selected comparables with adjustments, an estimated value range, market context, and a pricing recommendation with rationale — for a real-estate professional to review. Not a formal appraisal.

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。

Plan preparation for the Chinese postgraduate entrance examination (考研): choosing target schools and programmes with realistic odds, a phased study plan for the national subjects and the specialist papers, and preparation for the re-examination (复试) and adjustment (调剂). Use when asked 考研怎么规划, 帮我做考研复习计划, 择校择专业, 考研时间线, 复试怎么准备, 调剂, or plan my kaoyan. Produces a school-and-programme shortlist method with the data to collect, a month-by-month study plan by subject, weekly templates, the key-date checklist to confirm against official notices, and a 复试 and 调剂 preparation guide.

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。

Triage an incoming insurance claim: check the coverage trigger against policy wording, band severity and complexity, screen for fraud indicators, set a first-pass reserve range, and route with an SLA. Use when asked to triage a claim, review a first notice of loss (FNOL), assess a new claim, decide fast-track vs adjuster routing, or screen a claim for SIU referral. Produces a structured triage note with coverage view, severity band, indicator screen, reserve range, and a routing recommendation.

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 asked to check whether a figure is audited, which headline numbers sit outside the audited accounts, what 'adjusted' or 'underlying' means in this report, or who is vouching for the numbers in the highlights. For every headline figure in the highlights, chairman's statement and CEO review of an uploaded annual report, states whether it sits inside or outside the audited statements and what was added or removed versus the nearest audited number. Produces a boundary table, the list of figures outside the boundary, and a plain-English summary of who is vouching for what. Educational, not financial advice.

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。

Read UniFi Protect cameras, pull snapshots, and adjust camera settings (PTZ, lights, chimes). Trigger phrases: unifi protect, unifi camera, protect snapshot, unifi doorbell.

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。

Deep Codex-native Amazon FBA product validation using LaunchFast MCP and the LegacyX criteria. Use when the user wants a serious viability review for a keyword, niche, or adjacent niche expansion.

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。

Find and evaluate creators, influencers, affiliates, and UGC partners using audience fit, content fit, authentic product adjacency, engagement quality, public contactability, commerce signals, and brand-safety evidence—not follower count alone.

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 or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

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 p-value brackets, significance asterisks, and effect-size annotations to distribution plots using ggpubr, ggsignif, and statannotations with correct test selection (parametric vs non-parametric vs paired), multiple-testing adjustment, and rendering of negative results. Use when a boxplot/violin/raincloud needs in-figure statistical comparisons between groups.

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 volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.

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。

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。

Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.

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。

Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CA-CTCF/CA-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.

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。

Implements missing-data sensitivity analyses for confirmatory clinical trials including MMRM under MAR (with Kenward-Roger correction), reference-based multiple imputation (J2R, CR, CIR, LMCF per Carpenter-Roger 2013), Permutt delta-adjustment / tipping-point analysis, pattern-mixture identifying restrictions (CCMV, NCMV, ACMV), and the Cro vs Bartlett variance debate. Use when handling missing primary or secondary endpoint data in regulatory submissions following NRC 2010 and ICH E9(R1).

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。

Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.

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。

Prepares statistical reports for clinical trials following CONSORT 2025, SPIRIT 2025, ICH E9(R1) estimands, and FDA 2023 covariate adjustment guidance. Covers Table 1 generation, analysis populations (ITT/FAS/PP/Safety), the 5 ICH E9(R1) intercurrent-event strategies, MMRM under MAR (mmrm), reference-based MI (rbmi J2R/CR/CIR), Permutt tipping-point sensitivity, and Rubin's-rules vs frequentist variance debate. Use when preparing regulatory submissions, defining estimands, or implementing missing-data sensitivity analyses.

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。

Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.

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。

Read BEFORE troubleshooting or enhancing camera image quality. Diagnoses and enhances image quality from cameras connected via Image Acquisition Toolbox or USB Webcams support package. Discovers camera capabilities at runtime, analyzes captured images for quality issues (brightness, contrast, sharpness, noise, color balance, backlighting), suggests hardware setting adjustments tailored to the specific camera, and applies Image Processing Toolbox enhancement functions. Use when a user wants to improve camera image quality, troubleshoot dark/blurry/noisy/grainy/ overexposed/washed out/color cast images, or optimize camera settings.

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。

Caveman encoding for SPEC.md and spec-adjacent writes. Loaded by /spec, /build, /check. Cuts tokens ~75% vs prose while staying precise. Triggers on any write to SPEC.md or when user says "caveman", "compress this", "be brief".

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。

Amazon profit margin calculator for sellers. Calculate cost breakdowns, profit margins, break-even points, and get pricing recommendations. Supports single product analysis and batch calculations. Input product cost, shipping, FBA fees, and get instant profitability analysis. No API key required. Use when: (1) evaluating new product profitability before sourcing, (2) diagnosing existing product margins, (3) adjusting pricing strategy, (4) analyzing multiple SKUs at once.

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。

Set territory-specific pricing for subscriptions and in-app purchases using current asc setup, pricing summary, price import, and price schedule commands. Use when adjusting prices by country or implementing localized PPP strategies.

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。