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概要と使いどころ
Use this skill when planning, redesigning, or reviewing Xiaohongshu carousel visuals, covers, 3:4 image posts, page-by-page visual direction, image-generation prompts, style selection, layout critique, or XHS captions. It acts as a senior visual director for 小红书图文, not a generic copywriting assistant.
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。
Creates optimized multi-stage Dockerfiles for any language: separate build and runtime stages, minimal base images, layer caching, and non-root users. Use when containerizing an application or shrinking and hardening an existing image.
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 banners for social media, ads, website heroes, creative assets, and print. Multiple art direction options with optional generated or supplied visuals. Actions: design, create, generate banner. Platforms: Facebook, Twitter/X, LinkedIn, YouTube, Instagram, Google Display, website hero, print. Styles: minimalist, gradient, bold typography, photo-based, illustrated, geometric, retro, glassmorphism, 3D, neon, duotone, editorial, collage. Use when the user asks for a banner, cover image, ad creative, or website hero image.
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 design skill: brand identity, design tokens, UI styling, logo generation (55 styles, Gemini, Atlas Cloud, or MuAPI AI), corporate identity program (50 deliverables, CIP mockups), HTML presentations (Chart.js), banner design (22 styles, social/ads/web/print), icon design (15 styles, SVG, Gemini 3.1 Pro), social photos (HTML→screenshot, multi-platform). Actions: design logo, create CIP, generate mockups, build slides, design banner, generate icon, create social photos, social media images, brand identity, design system. Platforms: Facebook, Twitter, LinkedIn, YouTube, Instagram, Pinterest, TikTok, Threads, Google Ads. Use for brand, logo, icon, banner, slide, or social-image design work.
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。
Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS/MP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.
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 and convert images to WebP format. Checks public/assets/ for PNG/JPG files, verifies <img> tags have width/height, and generates cwebp conversion commands.
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。
トリアージで影響ありと判定された spec-drift issue の rubric/schema/template を同期したいとき、propose(read-only)で最小 Edit 差分と pre-image hash を提案し apply で監査 PASS と明示承認後に allowlist 対象だけを適用したいときに使う。
吉里吉里Z (kirikiri Z) 本体クラス API のリファレンス。TJS2 で吉里吉里Z 上のスクリプトを書く、レビューする、デバッグするときに使う。Layer / Window / Bitmap / System / Storages / Font / Plugins / Timer / Debug / AsyncTrigger / Scripts / BinaryStream / Matrix32 / Matrix44 / Rect / ImageFunction のコア API、サウンド系 (WaveSoundBuffer / SoundBuffer / VideoOverlay)、DrawDevice (BasicDrawDevice / SDLDrawDevice / OGLDrawDevice / GPUDrawDevice / NullDrawDevice)、OpenGL 描画系 (Canvas / Texture / ShaderProgram / Offscreen / VertexBinder / VertexBuffer)、および主要プラグイン提供クラス (HttpRequest / GdiPlus.* / WIN32Dialog / CSVParser / LineParser / Process / Pad / MenuItem / Unzip / Zip / PSD / SimpleHTTPServer) を網羅。**呼び出されたら必ず「共通パターン」と「クロスカッティング概念」を確認し、必要な詳細クラスは doc/reference/*.md を Read しに行くこと。** TJS2 言語そのものや組み込みクラス (Array / Dictionary / Math 等) は別 skill (`tjs2`) を参照。エンジン内部構造 (C++ 実装、レンダリングパイプライン詳細) は対象外。
Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional logo concepting, refined composition, sparse typography, strong symbolic meaning, premium mockups, art-directed imagery, and flexible grid layouts.
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。
Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".
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。
Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories. Triggers: "azure-containerregistry", "ContainerRegistryClient", "container images", "docker registry", "ACR".
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 multi-step AI content creation pipelines combining image, video, audio, and text. Workflow examples: generate image -> animate -> add voiceover -> merge with music. Tools: FLUX, Veo, Kokoro TTS, OmniHuman, media merger, upscaling. Use for: YouTube videos, social media content, market...
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。
Run 150+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search...
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。
Detect and prevent QR code phishing (quishing) attacks that bypass traditional email security by embedding malicious URLs in QR code images within emails.
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。
Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.
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。
Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.
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 forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash 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 or edit images via Gemini 3 Pro Image (Nano Banana Pro).
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。
Nuxt SEO meta-module with robots, sitemap, og-image, schema-org. Use when configuring SEO, generating sitemaps, creating OG images, or adding structured data.
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 workflow to establish where and when a photo or video was captured and whether it is authentic — evidentiary handling, metadata extraction, reverse image search for provenance, visual geolocation, chronolocation from shadows, and manipulation… Use when asked to verify where an image was taken, confirm or refute a claimed location or date, or authenticate media before relying on it
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 whether an image or video is authentic, original and correctly captioned — provenance checks, error level analysis, noise and JPEG compression analysis, clone and copy-move detection, lighting and shadow consistency, C2PA… Use when fact-checking a photo or video, checking for a deepfake or AI-generated image, spotting manipulation, or testing whether footage is recycled or miscaptioned
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。
Convert Markdown documents to PDF. Two modes available — (1) Playwright-based conversion with Mermaid diagram support for technical documentation, and (2) fpdf2-based professional PDF generation with cover pages, themed styling, and business-quality table formatting for estimates, proposals, and reports. Use this skill when converting Markdown to PDF, generating business documents from Markdown, or converting Mermaid diagrams to images. Triggers on short phrases like "PDFに変換して", "markdownをpdfにして", "PDFにして", "PDF化して", "PDF出力して", "convert to PDF", "make a PDF", or any request involving converting content to PDF format.
Domain knowledge for using PrintKK print-on-demand API tools effectively. PrintKK is a print-on-demand platform that lets sellers create custom products, manage designs, upload images, and fulfill orders via API. Use this skill whenever the user wants to interact with PrintKK — browsing products, creating designs, placing orders, managing images, or building integrations with the PrintKK API. Also trigger when the user mentions "PrintKK", "print on demand", "POD fulfillment", custom product design workflows, or wants to automate their PrintKK store operations.
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 and repair a kohya-style LoRA dataset before training. Use when the user asks to check, validate, inspect, fix, clean, 体检, or 修 a dataset, captions, or tags. Inspect image count, repeats and step budget, resolution and aspect buckets, duplicates, corrupt or non-RGB images, missing or empty captions, trigger consistency, and tag-frequency hygiene. Return a structural PASS, WARN, or FAIL report and apply only confirmed fix_dataset.py actions, dry-run first with displaced originals quarantined.
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。