4,283 件 ・ 関連度順
概要と使いどころ
[omh] Provider credentials without exposure: prepare provider-profile metadata without reading secrets or calling providers. Use when the user says: provider-profile-posture, provider profile posture, provider profile readiness, secret presence confirmation, connector profile posture, 공급자 프로필 상태, 시크릿 존재 확인, 커넥터 준비 상태.
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 you want to retrieve semi-quantitative protein expression and spatial localisation data from the Human Protein Atlas (HPA).
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。
Futuristic sci-fi aesthetic with dark themes, vibrant neon accents, and immersive spatial elements.
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。
Spatial depth design with isometric views, vanishing points, and layered elements that guide attention through 3D-like realism.
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 facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
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。
Create a Classic change, clarify its requirements, and obtain user confirmation. Use when the user invokes /comet-open or Classic enters Open or resumes initialization.
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。
Production-tested setup for Tailwind CSS v4 with shadcn/ui, Vite, and React. Use when: initializing React projects with Tailwind v4, setting up shadcn/ui, implementing dark mode, debugging CSS variable issues, fixing theme switching, migrating from Tailwind v3, or encountering color/theming problems. Covers: @theme inline pattern, CSS variable architecture, dark mode with ThemeProvider, component composition, vite.config setup, common v4 gotchas, and production-tested patterns. Keywords: Tailwind v4, shadcn/ui, @tailwindcss/vite, @theme inline, dark mode, CSS variables, hsl() wrapper, components.json, React theming, theme switching, colors not working, variables broken, theme not applying, @plugin directive, typography plugin, forms plugin, prose class, @tailwindcss/typography, @tailwindcss/forms
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。
Pre-mortem analysis that imagines a plan has failed, then works backward to identify causes and preventions. Use before launches, major decisions, or risky initiatives to surface hidden 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。
Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.
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。
Authenticate to Azure services using Azure Identity library for JavaScript (@azure/identity). Use when configuring authentication with DefaultAzureCredential, managed identity, service principals, or interactive browser login.
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。
Azure Identity library for Java authentication with Azure services. Use when implementing DefaultAzureCredential, managed identity, service principal, or any Azure authentication pattern in Java 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。
Build Azure Cosmos DB NoSQL services with Python/FastAPI following production-grade patterns. Use when implementing database client setup with dual auth (DefaultAzureCredential + emulator), service layer classes with CRUD operations, partition key strategies, parameterized queries, or TDD patterns for Cosmos. Triggers on phrases like "Cosmos DB", "NoSQL database", "document store", "add persistence", "database service layer", or "Python Cosmos SDK".
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。
Azure Communication Services common utilities for Java. Use when working with CommunicationTokenCredential, user identifiers, token refresh, or shared authentication across ACS services.
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。
Forecast Azure spend and price planned resources or workloads. WHEN: "forecast Azure spending", "project next month cost", "compare forecast to my planning target", "how much will Azure cost", "estimate VM cost", "compare storage tiers", "compare Azure regions", "retail price", "EA rate card", "negotiated pricesheet". DO NOT USE FOR: configured budget health or alerts, bill analysis, cost spikes, rightsizing, or commitments.
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。
Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.
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。
Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.
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。
Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.
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。
Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds.
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。
Analyzes isoform switching events and functional consequences using IsoformSwitchAnalyzeR. Predicts protein domain changes, NMD sensitivity, ORF alterations, and coding potential shifts between conditions. Use when investigating how splicing changes affect protein function.
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。
Statistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models.
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。
Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken.
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。
Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.
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。
Profile functional potential of metagenomes using HUMAnN3 and similar tools. Use when obtaining pathway abundances, gene family counts, or functional annotations from metagenomic data.
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。
Differentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types.
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。