1,584 件 ・ 関連度順
概要と使いどころ
Auto-annotate plasmids with features (promoters, terminators, resistance, origins, tags, fluorescent proteins) via BLAST against curated DBs (Addgene, fpbase, SnapGene). FASTA or raw sequence in; annotated GenBank, interactive HTML maps, CSV tables out. Handles circular topology. Use to verify synthetic constructs, prep Addgene submissions, share maps, or batch-annotate cloning libraries.
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。
Reviews code for security vulnerabilities and guides secure implementation using OWASP Top 10:2025, ASVS 5.0, the OWASP Top 10 for LLM Applications (2026), and the OWASP Top 10 for Agentic Applications (2026). Use when reviewing code or a diff for security issues, implementing authentication, authorization, sessions, or cryptography, handling untrusted input, files, or URLs, hardening config, dependencies, or CI, or building LLM and AI agent features.
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。
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
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 customer-focused marketing content from recent git changes, commits, merged pull requests, and shipped features. Use when the user invokes /diff-marketing or /marketing, or asks for blog posts, tweets, changelogs, or emails about what shipped.
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。
Provides comprehensive code review capability for NestJS applications, analyzing controllers, services, modules, guards, interceptors, pipes, dependency injection, and database integration patterns. Use when reviewing NestJS code changes, before merging pull requests, after implementing new features, or for architecture validation. Triggers on "review NestJS code", "NestJS code review", "check my NestJS controller/service".
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。
Provides comprehensive code review capability for Next.js applications, validates Server Components, Client Components, Server Actions, caching strategies, metadata, API routes, middleware, and performance patterns. Use when reviewing Next.js App Router code changes, before merging pull requests, after implementing new features, or for architecture validation. Triggers on "review Next.js code", "Next.js code review", "check my Next.js app".
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。
Provides comprehensive code review capability for React applications, validates component architecture, hooks usage, React 19 patterns, state management, performance optimization, accessibility compliance, and TypeScript integration. Use when reviewing React code changes, before merging pull requests, after implementing new features, or for component architecture validation. Triggers on "review React code", "React code review", "check my React 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。
Provides comprehensive React 19 patterns for Server Components, Server Actions, useOptimistic, useActionState, useTransition, concurrent features, Suspense boundaries, and TypeScript integration. Generates executable code patterns, validates security for public endpoints, and optimizes performance with React Compiler or manual memoization. Proactively use when building React 19 applications with Next.js App Router, implementing optimistic UI, or optimizing concurrent rendering.
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。
Databricks documentation reference via llms.txt index. Use when other skills do not cover a topic, looking up unfamiliar Databricks features, or needing authoritative docs on APIs, configurations, or platform capabilities.
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 for any task that spans more than a couple of steps, files, or sessions — refactors, migrations, multi-file features, anything that must survive a context reset or crash. Trigger when the user asks for a plan, says "keep track of this", "don't lose the thread", or when a task is large enough that forgetting step four is likely.
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。
API reference: SwiftUI. Query for views, layouts, navigation, @State/@Binding/@Observable, view modifiers, NavigationStack, iOS 26+ features.
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。
API reference: MapKit for SwiftUI. Map view, Marker, Annotation, camera positions, map features.
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。
Scores backlog items with RICE/WSJF/Kano and files GitHub issues for top candidates. Use when triaging a roadmap or prioritizing features for a sprint.
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。
Pick which precise covariance estimator to use for a given dataset. Use when you have data X and are unsure which estimator fits its dimension, conditioning, or tail behavior. Wraps precise.suggest() and covariance_features().
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。
Implement, review, or improve SwiftUI features using the iOS 26+ Liquid Glass API. Use when asked to adopt Liquid Glass in new SwiftUI UI, refactor an existing feature to Liquid Glass, or review Liquid Glass usage for correctness, performance, and design alignment.
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。
Guides Fiona workflows for GDAL-backed vector data I/O, GeoJSON-like features and schemas, CRS transforms, local virtual files, cloud-session boundaries, and the fio CLI.
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 working with Hugging Face Datasets: loading local or Hub datasets, defining Features schemas, processing/streaming datasets, converting formats, sharing to the Hub, managing cache/offline behavior, or using datasets-cli.
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 CLAM for weakly supervised computational pathology workflows on whole-slide images, from WSI patching through features, training, evaluation, and heatmaps.
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 CausalNex to learn causal structures, fit Bayesian networks, discretize features, generate synthetic graph data, and run inference, evaluation, and latent-variable workflows.
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。
Honey for plain Claude — the terse-prose core with no agent-harness features. Paste into a claude.ai Project's custom instructions, a Style, or an API system prompt. Strips filler, hedging, and pleasantries from every response while keeping facts, names, numbers, steps, and code exact. No tools required.
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。
POS (Point of Sale) architecture and patterns. Use when writing, editing, exploring, debugging, fixing bugs, implementing features, or reviewing WooPos-prefixed classes or files under ui/woopos/. POS uses a different architecture than the main app — plain ViewModel (not ScopedViewModel), pure Compose (no Fragments), Compose Navigation (no nav graphs), parent-child SharedFlow event bus. Loading this skill prevents applying main-app patterns that would be wrong for POS. For analytics use `pos-analytics`, for tests use `pos-tests`.
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。
Collaborative brainstorming mode for thinking through problems, ideas, and features before planning. Use when the user wants to discuss, explore ideas, or think through a problem without writing code.
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 authority by teaching the problem space, not announcing features. Use when the user finds "marketing" distasteful and does none, publishes only product updates, or wants a sustainable content and GitHub-README strategy that developers actually respect.
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。
Compares PIA/DPIA methodologies: CNIL PIA tool, ICO DPIA template, NIST Privacy Framework, and ISO 29134. Provides methodology selection criteria based on regulatory jurisdiction, organisation maturity, processing complexity, and resource availability. Covers regulatory acceptance, tool features, and cross-methodology mapping. Keywords: PIA methodology, CNIL, ICO, NIST Privacy Framework, ISO 29134, DPIA comparison, assessment.
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。