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概要と使いどころ
Use this skill before any creative or constructive work (features, components, architecture, behavior changes, or functionality). This skill transforms vague ideas into validated designs through disciplined, incremental reasoning and collaboration.
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。
Guidelines for modern Avalonia UI layout using Zafiro.Avalonia, emphasizing shared styles, generic components, and avoiding XAML redundancy.
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。
Complete guide for building beautiful apps with Expo Router. Covers fundamentals, styling, components, navigation, animations, patterns, and native tabs.
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。
Master Bevy's Entity Component System (ECS) in Rust, covering Systems, Queries, Resources, and parallel scheduling.
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。
Guidelines for modern Avalonia UI layout using Zafiro.Avalonia, emphasizing shared styles, generic components, and avoiding XAML redundancy.
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 content-focused websites with Astro — zero JS by default, islands architecture, multi-framework components, and Markdown/MDX support.
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。
Master modern Angular state management with Signals, NgRx, and RxJS. Use when setting up global state, managing component stores, choosing between state solutions, or migrating from legacy patterns.
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。
Master AngularJS to Angular migration, including hybrid apps, component conversion, dependency injection changes, and routing migration.
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。
Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive patterns.
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。
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
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。
Senior UI/UX Engineer. Architect digital interfaces overriding default LLM biases. Enforces metric-based rules, strict component architecture, CSS hardware acceleration, and balanced design engineering.
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。
Design, refine, implement, or review application UI and user journeys with minimal controls, clear actions, shared components, realistic prototypes, and polished behavior. Use for product screens, forms, onboarding, navigation, settings, and chat/task interfaces; not backend-only work or general graphic design.
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。
Parses Software Bill of Materials (SBOM) in CycloneDX and SPDX JSON formats to identify supply chain vulnerabilities by correlating components against the NVD CVE database via the NVD 2.0 API. Builds dependency graphs, calculates risk scores, identifies transitive vulnerability paths, and generates compliance reports. Activates for requests involving SBOM analysis, software composition analysis, supply chain security assessment, dependency vulnerability scanning, CycloneDX/SPDX parsing, or CVE correlation.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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。
Enforce a configuration-driven design system when generating UI. Ensures consistent spacing, colors, typography, dark mode, interactions, and accessibility across all AI-generated 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。
React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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。
Export code, terminal output, diagrams, or UI components to PNG images using headless browser rendering or CLI tools.
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。
Training patterns for autoregressive neural PDE solvers (FNO, DeepONet, CNO). Covers rollout training, noise injection for stability, multi-component loss functions (H1, frequency-sensitive, boundary-aware), per-channel normalization for coupled multi-variable systems, and the PDEBench nRMSE metric. Use when training any neural operator that predicts time-dependent PDE solutions.
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。
Orchestrates an end-to-end long-read structural-variant pipeline - basecalling to minimap2 alignment (platform-matched preset) to Sniffles2/cuteSV/pbsv calling to optional assembly-based calling (dipcall/PAV) to two-step .snf cohort merging to Truvari benchmarking - chaining ONT and PacBio HiFi runs while handing the SV signal mechanism off to the component skills. Use when running a long-read SV workflow from reads to a benchmarked callset, choosing the minimap2 preset and SV caller by platform and goal, deciding when long reads are worth it for the insertions and repeat-mediated SVs short reads physically miss, building a joint-genotyped cohort with the two-step .snf design, or parameterizing a Truvari benchmark against GIAB HG002 Tier 1 plus CMRG. Not for the SV signal mechanism itself (see variant-calling/structural-variant-calling) or short-read SV.
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。
Orchestrates the untargeted LC-MS metabolomics pipeline end-to-end (xcms 4.x feature extraction, QC/drift/normalization, confidence-stratified annotation, permutation-validated statistics, background-aware pathway mapping), naming what each stage decides and where it silently fails. Use when running a full LC-MS metabolomics study from raw mzML to enriched pathways and needing the honest handoffs between stages. Each stage defers to its component skill for parameters and traps; for stable-isotope flux (a separate branch, not this untargeted flow) see metabolomics/isotope-tracing.
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。
End-to-end flow, spectral, and mass cytometry (CyTOF) pipeline from raw FCS files to differentially abundant/expressed cell populations. Orchestrates the read -> compensate/unmix -> transform -> QC -> doublet-removal -> cluster-or-gate -> annotate -> diffcyt DA/DS chain with flowCore/CATALYST/diffcyt, branching on instrument type and on clustering-vs-gating. Use when processing a cytometry experiment end-to-end, deciding the pipeline path for an instrument, or wiring the flow-cytometry component skills into one analysis with valid sample-level statistics.
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。
Orchestrates the end-to-end germline short-variant pipeline from FASTQ to a filtered, normalized, benchmarked VCF, chaining QC/trim, BWA-MEM2 alignment, duplicate marking, optional BQSR, calling (bcftools/GATK HaplotypeCaller/DeepVariant/DRAGEN), normalization, site+genotype filtering, annotation, and hap.py/vcfeval benchmarking. Use when deciding the pipeline-wide reference-genome commitment (GRCh38 analysis set vs T2T, ALT/decoy handling), sequencing the steps in the defensible order (normalize BEFORE annotate, filter site- then genotype-level), choosing the calling engine and single-sample vs cohort joint-calling, picking a filtering strategy by cohort size, or benchmarking stratified within GIAB confident regions. Hands off mechanism to the variant-calling and read-alignment component skills; not a re-teach of any single step.
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。
Design system and premium UX rules for VenTu (ventu.surf). Use whenever building, restyling, reviewing, or polishing any frontend page, component, map, drawer, or motion. Overrides generic AI aesthetic defaults.
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。