上記に当てはまらないスキル（1.5万 件）
Analyze a company's published content to extract their brand voice, writing style, and tone guidelines. Reads 10-20 of their best content pieces and produces a brand voice profile covering tone, vocabulary level, sentence structure, formatting patterns, CTAs, and target persona. Useful before writing outreach, content, or campaigns that should match a client's existing voice.
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Prove a language switch translates what the user sees, keeps what they had already typed, and is still in force after a reload. Use when i18n, a locale picker, a language menu or translation files were added or changed, when a string is suspected of staying in the old language or showing a raw translation key, or when a user reports losing their form or their language choice after switching or refreshing.
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。
Verify that every interactive control on a page can take keyboard focus and that its primary action fires from the keyboard rather than only the mouse, and say plainly that Tab order cannot be verified today. Use when a component was hand-tested with a mouse, a custom dropdown/modal/combobox was added, or an accessibility report flags an unreachable control.
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。
Prove the next page of a list, or the next batch of an infinite scroll, loads NEW rows from the server, repeats none, and that the end of the list is handled. Use when pagination or infinite scroll was added or changed, when a list shows the same rows twice or skips some, or when the last page still offers a next control.
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。
Install, instrument and verify a web app end to end without pausing for a human. Use in an autonomous or goal-mode agent, in CI, or in any client that asks for approval on every command: it never says "restart your client" or "open a browser", because it takes a route that needs neither. Prefer the normal install-and-verify skill when a human is present and can answer.
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。
Ports HDInsight Spark and Hive workloads to Fabric, converting HiveContext and SparkContext to SparkSession, WASB and ABFS paths to OneLake shortcuts, Hive DDL to Delta tables in a Lakehouse, and Oozie coordinators and actions to pipeline activities and triggers. Use when the source workload is HDInsight.
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。
创建、设计、修改、重构和验证 AgentDock Skill 时使用；负责 Agent Skills 兼容的 SKILL.md、可移植核心、引用、辅助脚本、测试、安全边界和本地真实验证。
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。
审查、安装、配置、验证、更新和移除 AgentDock Skill 时使用；负责来源校验、安全评估、环境配置、content digest、精确 skill_ref 与当前 managed 内容验收。
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。
Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, TSL syntax and node materials, compute shaders, post-processing effects, and WGSL integration. Use this skill when working with Three.js WebGPU, TSL shaders, node materials, or GPU compute in Three.js.
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。
Navigate e-commerce sites to extract products, pricing, categories, and inventory. Handles pagination, variants, and JS-heavy storefronts.
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。
Compare two or more companies, products, or platforms across pricing, features, positioning, and docs. Use this skill whenever the user says "compare X vs Y", "how does X stack up against Y", "alternatives to X", "competitive landscape of …", "X vs Y vs Z", or asks for a competitor matrix. Uses search to discover competitors when the user only names a category, then scrape for each competitor's homepage, pricing page, and features/docs. Returns a normalized comparison matrix as JSON.
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。
Common steps for building multi-layer system architecture models using System Composer. Use when implementing architecture models or when interacting with interface dictionaries, allocation sets, stereotypes, and requirements for architecture components.
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, show, open, or identify shipped MathWorks example models that demonstrate a concept, block, or application domain, and return the command to open them.
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Estimate frequency response from Simulink models using frestimate. Use when frequency response should be obtained from simulation rather than model linearization.
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。
Linearize Simulink models. Use when obtaining linear time invariant or linear parameter varying models from Simulink 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。
Microsoft.Extensions.Options patterns including IValidateOptions, strongly-typed settings, validation on startup, and the Options pattern for clean configuration management.
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。
Critical Akka.NET best practices including EventStream vs DistributedPubSub, supervision strategies, error handling, Props vs DependencyResolver, work distribution patterns, and cluster/local mode abstractions for testability.
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Choosing the right concurrency abstraction in .NET - from async/await for I/O to Channels for producer/consumer to Akka.NET for stateful entity management. Avoid locks and manual synchronization unless absolutely necessary.
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。
Choose the right serialization format for .NET applications. Prefer schema-based formats (Protobuf, MessagePack) over reflection-based (Newtonsoft.Json). Use System.Text.Json with AOT source generators for JSON scenarios.
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。
Entity Framework Core best practices including NoTracking by default, query splitting for navigation collections, migration management, dedicated migration services, and common pitfalls to avoid.
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。
Assembles genomes de novo from noisy long reads (Oxford Nanopore R9/R10/Dorado, PacBio CLR) with Flye (repeat graph), Canu (correct-trim-assemble OLC), NextDenovo, Shasta, Raven, wtdbg2, or miniasm, and reconciles bacterial assemblies into a consensus with Trycycler/Autocycler. Covers matching the input flag to the basecaller era (--nano-hq vs --nano-raw), why a raw long-read assembly is contiguous but low-QV and not finished until polished, haplotig false-duplication and purge_dups, coverage and read-N50 as non-substitutable inputs, and mid-read adapter de-chimerization. Use when assembling a bacterial or eukaryotic genome from ONT or PacBio noisy reads, choosing a long-read assembler, or diagnosing an over-collapsed or duplicated assembly. For PacBio HiFi use hifi-assembly 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。
Assembles microbial-community sequencing into metagenome-assembled genomes (MAGs) with metaFlye (ONT), metaSPAdes/MEGAHIT (Illumina), and hifiasm-meta/metaMDBG (PacBio HiFi), then recovers genomes via multi-binner consolidation (MetaBAT2, MaxBin2, CONCOCT, SemiBin2, VAMB -> DAS_Tool) and QCs them against MIMAG with CheckM2, GUNC, and GTDB-Tk. Covers why a metagenome is not a genome (uneven coverage, micro-diversity, strain collapse to consensus), differential-coverage binning, co-assembly vs per-sample, the rRNA-operon collapse that fails short-read MAGs, and strain resolution with inStrain. Use when reconstructing genomes from a microbiome, soil, ocean, or gut community, recovering MAGs, or resolving strain-level variation.
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Nominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas9, eSpCas9, SpCas9-HF1). Use when assessing guide RNA specificity, choosing among candidate guides, screening a therapeutic guide against population variation, or planning empirical off-target validation. Distinguishes predicted vs detected vs validated. On-target activity scoring and deaminase (Cas-independent) base/prime-editor off-targets are separate skills.
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Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.
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。