1.2万 件 ・ 関連度順
概要と使いどころ
사용자와 단계별로 검토하는 guided design harness. Discovery→Wireframe→Components→Microcopy→Validation을 omd-master가 실행하고 journey/system/validation의 필수 체크포인트에서 멈춘다. '/omd-harness', '체크포인트마다 검토', '나와 단계별로 디자인', 'guided design' 요청에 사용. 질문 없이 원샷으로 새 제품을 자율 구축하는 요청은 omd:autopilot, 단일 컴포넌트 수정은 omd:apply.
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。
사용자와 단계별로 검토하는 guided design harness. Discovery→Wireframe→Components→Microcopy→Validation을 omd-master가 실행하고 journey/system/validation의 필수 체크포인트에서 멈춘다. '/omd-harness', '체크포인트마다 검토', '나와 단계별로 디자인', 'guided design' 요청에 사용. 질문 없이 원샷으로 새 제품을 자율 구축하는 요청은 omd:autopilot, 단일 컴포넌트 수정은 omd:apply.
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。
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
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。
Displays chronological events and activity through timelines, activity feeds, Gantt charts, and calendar interfaces. Use when showing historical events, project schedules, social feeds, notifications, audit logs, or time-based data. Provides implementation patterns for vertical/horizontal timelines, interactive visualizations, real-time updates, and responsive designs with accessibility (WCAG/ARIA).
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。
Drive Chrome via the DevTools Protocol from JavaScript. Run JS snippets through the `browser-harness-js` CLI — it auto-spawns a long-lived bun HTTP server holding a fully-typed CDP `Session`, and every call (`browser-harness-js 'await session.Page.navigate(...)'`) executes against the same persistent connection. Session, active target, and globals survive across calls. Use when the user wants to automate, script, or inspect a Chrome browser via CDP — single tab or multi-tab, attach to existing Chrome or to a new one launched with --remote-debugging-port.
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 .NET AI agents, harnesses, and multi-agent workflows with Microsoft Agent Framework using the right agent type, sessions, tools, workflows, hosting protocols, and enterprise guardrails. USE FOR: building or reviewing .NET code that uses Microsoft.Agents.*, Microsoft.Extensions.AI, AIAgent, HarnessAgent, AgentSession, or Agent Framework hosting packages; choosing agent, harness, workflow, and hosting shapes. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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 data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.
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。
Translate technical analysis into business language. Use when explaining statistical concepts to non-analysts, simplifying technical findings, or bridging communication between data teams and business stakeholders.
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。
Translate SQL queries into plain language business logic. Use when documenting queries, explaining analysis to non-technical stakeholders, code reviewing for correctness, or building a query catalog.
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 SEO/GEO/AEO analysis toolkit for optimizing content visibility across traditional search engines (Google, Bing), AI platforms (ChatGPT, Perplexity, Claude, Gemini, Grokipedia), answer engines (Google AI Overviews, Bing Copilot, featured snippets), voice assistants (Google Assistant, Siri, Alexa), and social media (Facebook, Twitter, LinkedIn, WhatsApp, Instagram). Analyzes HTML/Markdown/JSX files for metadata completeness, schema markup, keyword optimization, entity extraction, and generates multi-format audit reports with platform-specific recommendations.
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 reviewing, designing, or modifying Java enterprise systems that expose, exchange, store, process, export, or port data across users, businesses, connected products, cloud providers, APIs, event streams, AI data pipelines, data spaces, or SaaS platforms and need EU Data Act engineering controls. This should trigger for requests such as Review a Java platform for EU Data Act controls; Design data access and portability evidence; Add data-sharing request workflows, export formats, interoperability, metadata, audit logs, cloud-switching support, non-personal data safeguards, or trade-secret handoffs; Assess Data Act engineering readiness before production release. Part of Plinth Toolkit
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 headlines, titles, and subject lines: charge, volume, tighten.
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。
Audit applications and infrastructure handling Protected Health Information against HIPAA — Security Rule (administrative, physical, technical safeguards), Privacy Rule, Breach Notification Rule, plus HITECH. Covers ePHI scoping, the 18 HIPAA identifiers, Business Associate Agreement (BAA) chain-of-liability, minimum-necessary standard, and breach notification timing. Use when the user mentions 'HIPAA,' 'HIPAA Security Rule,' 'HIPAA Privacy Rule,' 'PHI,' 'ePHI,' 'protected health information,' 'BAA,' 'business associate agreement,' 'covered entity,' 'business associate,' 'minimum necessary,' 'HIPAA breach,' 'HITECH,' 'healthcare compliance,' 'medical data,' 'patient data,' or audits any system that creates, receives, maintains, or transmits PHI.
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。
Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the security slice). Use when the user mentions 'AI risk,' 'AI governance,' 'NIST AI RMF,' 'AI compliance,' 'ML governance,' 'model risk management,' 'AI fairness,' 'AI bias,' 'algorithmic accountability,' 'AI Bill of Rights,' 'EU AI Act,' 'AI transparency,' 'model card,' 'AI red team,' 'AI safety,' 'responsible AI,' 'model drift,' 'concept drift,' 'AI monitoring,' 'AI incident,' or needs to assess or govern an AI / ML system.
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。
NestJS best practices and architecture patterns for building production-ready applications. This skill should be used when writing, reviewing, or refactoring NestJS code to ensure proper patterns for modules, dependency injection, security, and performance.
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。
HTML, CSS, and JavaScript craft guidelines from bendc/frontend-guidelines — semantics, a11y basics, selector discipline, motion-friendly CSS, and readable JS. Use for markup review, static HTML/CSS, email HTML, or general front-end hygiene; pair with repo frontend rules and TypeScript lint policy.
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。
Guide to interpreting BUSCO completeness statuses: why Duplicated BUSCOs count as complete, parsing output files, computing/comparing completeness across proteomes/genomes, common counting mistakes. Use when running BUSCO QC, comparing assemblies, or reporting completeness. See also: prokka-genome-annotation for annotation workflows feeding BUSCO.
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。
Dataflow workflow engine for scalable bioinformatics pipelines. Defines processes (containerized tasks) connected by channels; runs local, HPC (SLURM/SGE), cloud (AWS/GCP/Azure), or Kubernetes via a single config change. Powers nf-core. Use Snakemake for rule-based Python workflows; use Nextflow for containerized, cloud-native, and nf-core pipelines.
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。
Review IEEE conference, journal, Transactions, Letters, or magazine manuscripts from a technical reviewer perspective using routed IEEE review gates. Use when evaluating scope fit, novelty, validity, data, clarity, compliance, advancement, engineering significance, method soundness, experiment sufficiency, baseline fairness, figure/table quality, formatting risks, rejection risk, major revision strategy, pre-submission readiness, or the quality and specificity of reviewer comments that may be generic, careless, contradictory, or possibly AI-generated.
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, audit, and strengthen experimental validation for IEEE manuscripts using routed claim-evidence checks. Use when planning experiments, selecting baselines, designing ablations, choosing metrics, checking fairness, adding robustness tests, explaining results, analyzing complexity, building claim-to-evidence matrices, or responding to reviewer concerns about insufficient experiments.
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。
Architecture patterns, design principles, and proven recipes for building robust robotics software. Use this skill when designing robot software architectures, choosing between behavioral frameworks, structuring perception-planning-control pipelines, implementing state machines, designing safety systems, or architecting multi-robot systems. Trigger whenever the user mentions behavior trees, finite state machines, subsumption architecture, sensor fusion, robot safety, watchdogs, heartbeats, graceful degradation, hardware abstraction layers, real-time constraints, or software architecture for robots. Also applies to sim-to-real transfer, digital twins, and robot fleet 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。
Turn Chinese classical poems and ci into coherent vertical Chinese-art videos with poem-driven scene grouping, GPT ImageGen stills, Docker-only Gemini I2V, retained model-generated ambience, Gemini sparkle-watermark cleanup, brush-calligraphy captions revealed character by character, optional local BGM mixing, stitching, and final-frame QA. Supports verse-driven variation across ink landscape, gongbi bird-and-flower, colored figure-and-horse painting, blue-green landscape, xuan paper, silk, and related Chinese visual languages. Use when users ask for 古诗词动态视频、诗词逐句或两句一景、国风视频、毛笔字逐字出现、整首诗拼接成片，or want the established 月落乌啼霜满天 workflow applied to another poem.
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 NestJS framework patterns with Drizzle ORM integration for building scalable server-side applications. Generates REST/GraphQL APIs, implements authentication guards, creates database schemas, and sets up microservices. Use when building NestJS applications, setting up APIs, implementing authentication, working with databases, or integrating Drizzle ORM.
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。
Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL, including Auto CDC from event streams or periodic complete snapshots. Invoke BEFORE starting 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。