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概要と使いどころ
AI/ML Workflow Bundle workflow skill. Use this skill when the user needs AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
当用户想把 HTML 幻灯片转成 .pptx 时触发——"HTML 转 PPT"、"网页幻灯片转 pptx"、"把这个 deck 转成 ppt"、"给同事一份 ppt 副本"、"汇报不方便放浏览器"、给了 .html 路径 + 提到 ppt/pptx/演示/幻灯片。保留矢量文字可编辑、字体按需嵌入、复杂装饰自动走截图兜底；不依赖 reveal.js / 自写 deck 等具体框架。
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。
The alphabetical-sort invariants the hyperlane-registry CI / CodeRabbit enforces on warp route YAML (deploy.yaml and config.yaml) — top-level chain entries sorted by chain name, and keys within each entry in strict alphabetical order — plus the canonical per-file key orderings and a verification pass. Referenced by any warp deploy/update skill that writes or edits a registry YAML file.
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。
MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.
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 polished single-file HTML artifacts for plans, reports, explainers, code reviews, lightweight dashboards, prototypes, and one-off editing tools. Use when the user asks for a standalone HTML file, single HTML, HTML artifact, interactive report, shareable local artifact, Alpine.js artifact, custom CSS artifact, copy/export UI, or a browser-openable deliverable instead of Markdown.
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。
为 AI Agent 提供基于 shadcn/ui 极简现代美学的单文件 HTML 组件化规范与母版体系。当用户要求“生成 HTML 页面/网页”、“前端可视化大盘/看板”、“面试/分析/测试报告”、“对比矩阵/方案比对”、“review 结果做成可视化页面”、“代码审查报告 / 把 diff 做成可视化审查页”、“不要输出 markdown 墙/不要文字墙”、“把分析结果做成可交互单文件网页”等场景时，务必使用本 Skill。零构建、零 npm、严禁引入外部 CDN（无断网白屏风险），纯原生 HTML/CSS/SVG，支持暗黑模式与双击秒开。
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。
Transform Markdown or text specifications into human-readable HTML documents with summaries, reordered structure, visual explanations, diagrams, charts, and traceability to the source. Use when asked to convert a spec, requirements document, API spec, product spec, design doc, or technical document into readable HTML, especially when the user wants summarization, restructuring, diagrams, graphs, images, or visual aids rather than a literal Markdown-to-HTML conversion.
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。
把 guizang-ppt-skill 生成的 HTML 横向翻页 deck 转成 deck.pdf 和 deck.pptx，供线下演讲、加速器/评委上传、印刷等场子使用。**只要用户提到 HTML deck 转 PPT、deck 导出 PowerPoint、网页 PPT 转 pptx、HTML 转 PowerPoint、把幻灯片/演讲材料/演示文件做成离线版、需要 .pptx 上传，就用这个 skill**，即使用户没说 "guizang" 字样。产出是图片型 PPTX（每页一张 PNG 满版，视觉 100% 还原但内文不可编辑）。
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 a structured workflow for migrating an Android XML View to Jetpack Compose. This skill details the step-by-step process, from planning and dependency setup, to theming and layout migration, validation and XML cleanup. Use this skill when you need to migrate an XML View to Jetpack Compose in an Android project. It solves the problem of converting the UI of a legacy XML View into modern, declarative Compose components while maintaining interoperability.
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。
Detects version drift between machine-readable law YAML files and the binding geldende wettekst on wetten.overheid.nl at the YAML's claimed valid_from. Catches the silent failure where authored content lags behind ingevoerde wijzigingen — a YAML dated 2025-01-01 still carrying pre-Stb.-X text. Activate as Step 0 of every regelrecht-stelselanalyse micro-cycle, before any YAML edit. Strict by design: no bypass, no normalization beyond whitespace within a single paragraph; the geldende wettekst is leading and is mirrored verbatim, including errata. Use proactively when starting a stelselanalyse cycle, before harvest/migrate/extend, or when user mentions "drift", "versie", "geldende tekst", or worries that a YAML may be stale relative to wetten.overheid.nl.
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。
D-ZERO の HTML/Pug マークアップ規約。HTML や Pug を書く・編集する・レビューするときに使う。コンポーネント設計、クラス命名、文書構造、画像、リンク、メタ情報に適用する。
Use when setting up or auditing how compliance documentation is structured, collected, and preserved for regulatory audit in Salesforce FSC — covering KYC data collection workflows, AML screening integration setup, audit trail configuration, and regulatory reporting readiness. Triggers: KYC form setup, AML integration configuration, compliance data collection, audit trail setup, regulatory documentation workflow, Field Audit Trail configuration, Setup Audit Trail, Event Monitoring, Discovery Framework, FSC KYC objects, Identity Document setup, Party Identity Verification. NOT for security implementation, NOT for designing AML/KYC architecture (use architect/aml-kyc-process-architecture), NOT for configuring who can access deal or client data (use admin/compliant-data-sharing-setup).
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。
Render an HTML/CSS/JS template to a local MP4 video by capturing frames with headless Chromium (via Playwright) and encoding them with ffmpeg. Use when "render my animation as a video", "export HTML to MP4", "make a video from my web template", or "capture CSS animation as MP4". NOT for recording live browser interactions (use `agent-browser record start`); NOT for screen recording; NOT for AI video generation (use mk:multimodal).
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 pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.
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。
把多页 HTML 报告(主页 + N 子页 + PNG 资产)合并为单一独立 HTML / PDF / XLSX 三件交付物。 Use when finalizing a disease market sizing report and need shareable deliverables — single-file HTML for email/IM(零依赖,双击浏览器开), optional PDF for print/archive, XLSX for data export. 领域无关、跨疾病通用。 下游 of disease-market-sizing-html-template;上游 of email/IM/print 分发。 Use proactively whenever the user says "做交付物" / "生成 PDF" / "导 Excel" / "合并单文件 HTML"。
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 working with Bentoml — bentoML model serving and packaging management. Covers service management, model packaging, deployment status, API testing, Bento building, and runner configuration. Use when packaging ML models for serving, deploying BentoML services, debugging inference issues, or managing model artifacts.
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。
This skill should be used when the user asks to "test for HTML injection", "inject HTML into web pages", "perform HTML injection attacks", "deface web applications", or "test content injection vulnerabilities". It provides comprehensive HTML injection attack techniques and testing methodologies.
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 pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.
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 pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.
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 production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
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 comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
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。
This skill should be used when the user asks to "test for HTML injection", "inject HTML into web pages", "perform HTML injection attacks", "deface web applications", or "test content injection vulnerabilities". It provides comprehensive HTML injection attack techniques and testing methodologies.
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。
参考素材の見た目やカットのリズムを再利用できる設定に整理し、手元の映像の編集計画、画像・動画・3D素材の生成計画、編集可能なタイムラインをオフラインで作るスキル。
機械学習モデルを本番で使うために、データの仕様、再現可能な学習、品質評価、配備、監視、切り戻しを整理し、実装計画やレビュー項目にまとめるスキル。