1,392 件 ・ 関連度順
概要と使いどころ
Deep single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says "analyze this page", "check page SEO", "single URL", "check this page", "page analysis", or provides a single URL for review.
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 building exhibits for an American Anthropologist (AA) manuscript — photographs, maps, site plans, artifact images, kinship diagrams, transcripts, evidence tables, and multimodal media — so they are self-contained, accessible (alt text), ethically cleared (consent, permissions), and count toward the word budget. Designs exhibits; it does not run analysis or clear permissions for you.
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。
Generates Instagram-ready product reels from any e-commerce product page URL. Scrapes product images, classifies by type, generates AI-animated clips via Higgsfield API, creates text overlays with style presets, and composes a 15-20 second reel with music. Supports model-based and product-only reels.
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 pixel-accurate Apple Notes (iPhone, light mode) screenshot mockups from a JSON note spec. Outputs HTML + PNG at the iPhone 16/15 Pro native 1180×2556. Supports paragraphs, images, checklists, dividers, autocorrect underline, smart quotes, and an optional iOS keyboard chrome overlay used by the parent video-ad molecule.
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 talking head videos from any source material (docs, changelogs, blog posts, notes, transcripts). Produces multi-scene videos with avatar narration over screenshots/images using HeyGen v2 API. Supports Quick Shot and Full Producer modes.
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。
Analyze images with AI - extract text, describe content, detect objects
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 LinkedIn carousel posts as high-quality PNG images. Design informational multi-slide posts like "5 AI GTM workflows" with consistent styling, then automatically screenshot each slide at LinkedIn's optimal 1080x1080px format.
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 FigJam/Miro-style workflow diagrams as high-quality PNG images from plain-text workflow descriptions. Renders beautiful HTML diagrams with connected nodes, arrows, and labels, then screenshots them for sharing.
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。
Portable visual skill pack for the Agent Skills ecosystem (Claude Code, Claude Desktop, Claude Cowork, Claude Design, Goose, Cursor, Codex). Discovers community-published styles + formats via the gooseworks CLI, runs an extract-style workflow on reference images, and exports rendered PNGs via Playwright.
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。
Final QC gate for a rendered 9:16 video ad, run before it is published. One script checks what a machine can decide — exact 1080x1920 size, a hook that moves and speaks in the first second, no frozen stretches, no dead air, no black frames, the brand's real logo on the end card (never a favicon, never a redrawn or wrong logo), end-card colours near the brand palette — and builds one contact sheet (frames with the TikTok/Reels UI safe zones shaded, beside the logo, product images and a font specimen) for the checks that need eyes — font, product likeness, product consistency across scenes, safe zones. A declared silent-text profile covers silent, text-led formats such as kinetic text (no audio track needed, short blank beats between text beats allowed within fixed bounds). Exit 0 PASS / 2 FAIL / 3 ERROR. Use on every finished video master before pinning 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。
Animate an existing product image or static ad into a short branded video using the GooseWorks Animate Images workflow, with source-image continuity and optional end-frame control.
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 a 'model comparison grid' video from a config — a fal-style "same prompt, N contenders" showcase — a dark real-DOM stage where per beat a monospace prompt fades in centered, docks to a small top strip, then a labeled 2-4 panel grid (static images OR muted video clips, mixable per cell) staggers in and holds for comparison, plus a minimal end card — frame-stepped via Playwright (video cells are frame-seeked deterministically) and encoded with FFmpeg. Deterministic assembly, FREE (cell media comes from create-image-fal / create-video-fal, music from create-music-elevenlabs), text stays pixel-crisp. Use for the model-comparison-grid format.
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。
Assemble a viral iOS "AirDrop" notification-carousel video ad (≈6–8s, 9:16) from a brand line plus 6–16 real product photos — a native AirDrop share-sheet card ("Brand would like to share a ___ · Decline / Accept") springs up and its preview window CYCLES through the products, landing on a range/lineup payoff with an Accept tap; a chime + soft per-swap ticks track the swaps. DETERMINISTIC assembly — an HTML card (real DOM text) rendered to PNG via headless Chrome, chroma-keyed, its magenta window refilled per-product in PIL, then animated + audio-synthed with FFmpeg. FREE (no paid model calls); the recipe supplies the brand line, product images, and payoff and gates the only optional paid step (a hero shot when the brand has NO usable photo → create-image-fal). Use for the airdrop-notification-carousel format.
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 generating sexy/NSFW or posed images with local Qwen-Image 2.1 — pose/NSFW LoRAs (tawsLoRa femaledVaginus, TheseAlpacas, posture-v3), pose transfer from an existing image, or exact pose control via VNCCS PoseStudio 3D pose library. Triggers: 性感姿势, 摆姿势, NSFW 出图, 换姿势, 姿势控制, pose transfer, pose lora, VNCCS, 人偶姿势, 姿势库. Independent of any general image-generation skill (which handles SFW text/edit/cutout 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。
Build presentation plans for PPT / slides / decks through a conversation-first workflow, then propose multiple visual directions with preview images before writing deck specs. Use when the user asks to create a PPT, presentation, deck, 答辩稿, 路演 deck, 产品介绍 PPT, 汇报 PPT, or when the user has only a topic or rough materials and needs help clarifying structure, style, and page planning before generation.
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。
Publish .NET services as container images with the built-in SDK tooling (dotnet publish /t:PublishContainer, Microsoft.NET.Build.Containers) - no Dockerfile required. Covers the MSBuild property surface, base-image inference, self-contained and AOT implications, and CI/CD publishing.
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。
Segment single cells from multiplexed IMC/MIBI tissue images using Mesmer/DeepCell, Cellpose, or ilastik+CellProfiler, covering whole-cell vs nuclear segmentation, the summed-membrane-channel decision, nuclear-expansion bias, lateral spillover, resolution-floor parameters, and downstream-proxy evaluation. Use when delineating cells after preprocessing, choosing a segmentation model, building a cell mask for quantification, diagnosing impossible double-positive populations, or troubleshooting over/under-segmentation.
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 photo picking, camera capture, and media handling in iOS apps using PhotoKit and AVFoundation. Use when working with PhotosPicker, PHPickerViewController, camera capture sessions (AVCaptureSession), photo library access, image loading and display, video recording, or media permissions. Also use when selecting photos from the library, taking pictures, recording video, processing images, or handling photo/camera privacy permissions in Swift apps.
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 whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
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 whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
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。
Display 3-D image volumes, medical image volumes, surface meshes, and annotations for 3-D image processing. Use when displaying 3-D images or isosurfaces with volshow, creating volume viewers with viewer3d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming volumetric data, or building apps with volume display.
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 OCR pipelines in MATLAB using the ocr() function. Use this skill when the user wants to read text from images, documents, signs, meters, displays, license plates, gauges, receipts, or seven-segment displays. Covers image preprocessing, text detection (CRAFT, MSER), ROI-based recognition, multi-language OCR, and custom model training. Use when: OCR, text recognition, extract text from image, character recognition, document scanning, meter reading, gauge reading, receipt scanning, digitize text from photo.
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 to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced 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。
Detect, respond to, and prevent software supply chain attacks on package registries, container images, and CI/CD pipelines with lockfile auditing, provenance verification, and emergency response playbooks.
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。