3,180 件 ・ 関連度順
概要と使いどころ
Generate extremely simple, cute, personified square character images with rounded heavy forms, two purposeful character colors, one solid background color, and a dominant lower-corner composition. Use when creating an animal, creature, robot, ghost, plant, object, or other character image, including when the agent should infer three product-relevant directions and propose six independent candidates for approval.
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。
Environment diagnostic skill for SenseNova-Skills project. Checks that sn-image-base is properly installed and configured, validates dependencies and environment variables. Prompts user to configure missing required variables and saves them to .env file. After configuration, reloads environment and suggests agent restart if needed.
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。
Base-layer skill for the SenseNova-Skills project, providing low-level APIs for image generation, recognition (VLM), and text optimization (LLM). This skill does not preprocess inputs; it only calls backend services and returns results. This skill is not user-facing and is intended for upper-layer skills only.
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 another PPT Skill needs web search, image search or download, or image generation and the host Agent's equivalent native capability is absent or has failed.
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 a prepared PPT task with a current outline.md should be generated as an image-first presentation with one 16:9 image per slide and optional PPTX packaging.
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。
External recon for software supply-chain attack surface — package-namespace squatting candidates, dependency-confusion vulnerabilities, GitHub Actions injection openings, container image registry exposure, SBOM mining, internal-package-name leakage, and CI/CD configuration exposure. Reconnaissance and identification ONLY — actual package publishing / typosquat attacks are EXTERNAL-OFFENSIVE and require explicit written sign-off because they can affect the entire npm/PyPI ecosystem. Use when the target has a public GitHub org, when their build artifacts/SBOMs are reachable, when their docker images are on Docker Hub/GHCR, or when you find internal package names in their JS bundles.
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。
Hunt Next.js specific vulnerabilities — Server Actions arbitrary function execution, Middleware auth bypass via static asset paths, ISR cache poisoning, Image Optimization SSRF (/_next/image), RSC payload leakage, getServerSideProps injection, source map exposure, debug endpoint leakage. Use when target runs Next.js 13/14/15 or any React SSR framework.
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 publication-quality PNG chart images from data, supporting line, bar, area, candlestick, pie, and heatmap charts. Triggers when the user asks to visualize data, create a graph, plot a time series, or generate a chart for a report, alert, or dashboard. Runs as a lightweight, headless Node.js process without a browser.
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 professional slide deck images from academic papers and content. Creates comprehensive outlines with style instructions, auto-detects figures from PDFs, then generates individual slide images. Use when user asks to "create slides", "make a presentation", "generate deck", or "slide deck" for papers.
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。
Plan and build 2D game maps and scenes - top-down and side-scrolling levels, tilemaps, prop packs, parallax backgrounds and HD-2D battle or story plates - with collision, navigation and reachability checks, environment motion, a playable HTML preview and export to Tiled, Godot 4 or LDtk. Map art (terrain, tiles, props, plates, parallax layers) is image generation by default, through generate2dmedia route_media.py (the API when a key is configured, else the local Codex or Grok CLI); map data (layout, collision, exits, spawns) is written as data. Use for any map, level, room, scene, background, terrain or prop-kit request. Not for characters, creatures, items or FX (generate2dsprite), character animation (video2dsprite), code-drawn tiles or maps (codeart2d, only on request) or calling an image or video API by itself (generate2dmedia).
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。
Extract text from images and scanned documents using PaddleOCR - supports 100+ languages
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。
Read 小红书 / RedNote posts and image notes from xhslink.com or xiaohongshu.com using available web or browser tools. Distinguish captions from image content.
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。
使用内置 image_generate.py 脚本生成图片, 准备清晰具体的 `prompt`。
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。
Plan, validate, and safely publish Google Ads assets, including sitelinks, callouts, structured snippets, image assets, and Performance Max asset briefs. Use when asked for Google Ads assets, ad extensions, sitelinks, callouts, snippets, image assets, Performance Max assets, PMax creative, or an asset audit.
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。
生成电商商品视觉方案：主图概念、场景图、详情页视觉方向和 AI 生图 Prompt。 当用户说"商品主图""详情页视觉""电商配图方案""商品场景图""带货视觉""产品视觉方向""详情页设计"时使用。 本 SKILL 出视觉方案+生图 Prompt（策划）；实际抠白底图用 remove-bg，实际生成图片用 ai-image-gen。
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。
批量处理：对一个目录里的一批图片/视频/音频统一套用同一操作——批量压缩、加水印、转格式、缩放、转比例、音量归一化等。当用户说 批量处理、批量压缩、批量加水印、批量转格式、一批图片/视频、给这个文件夹、全部转成、批量缩放、批量转竖版、整个目录 时使用。基于 shared/scripts/batch_process.py（委派 image_ops/video_ops/audio_ops）。与 image-editing/video-editing/audio-editing 区别：那些处理单文件，本 SKILL 批量套用到整个目录。
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。
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology 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。
Convert Markdown to WeChat Official Account HTML. Use this whenever the user wants WeChat article formatting, article preview, WeChat draft upload, image generation for articles, cover or infographic generation, image-post creation, writer-style drafting, title suggestions, AI trace removal, or current discovery of supported providers, themes, prompts, and layout modules. Also use for unpublished Zhihu, CSDN, Toutiao, or Tencent Cloud Developer Community drafts through a browser. Use for product introductions, encyclopedia articles or entry drafts, and writing tailored to platforms or search targets.
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 the user asks to "analyze video", "watch this video", "what happens in this video", "describe this clip", "review this footage", "classify these videos", "compare videos", "analyze this image", "what's in this screenshot", or when the user provides a video/image file path and expects visual understanding. Also trigger on: "qwen", "video bridge", "multimodal analysis", "motion analysis", "video reference", "video breakdown", "batch classify", or any task requiring understanding of video content that Claude cannot do natively.
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 a photo into a specific AI look (new hairstyle, collectible figurine, royal pet portrait, wedding photo, 90s yearbook, sticker, HD restore) or a photo into a dance video, using Kavel (www.kavel.ai). Use when the user wants to restyle/transform a photo, asks "how do I make an AI X from my photo", or wants a photorealistic image edit that keeps the same face. Gives the right tool, a model-tuned prompt, and the generate link.
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。
Discover Atlas Cloud image and video models, inspect their live schemas, and submit one confirmed media generation request with bounded GET polling. Use when integrating Atlas Cloud media APIs or generating images and videos without hard-coding stale model parameters.
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 after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
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。
Running an in-bundle DOCA service as a container on a BlueField (Argus, DMS, Firefly, or UROM service): kubelet standalone watching a static-pod manifests directory, YAML pod-spec drop, kubelet status / ENTRYPOINT logs / per-service liveness. Trigger on "how do I run my built service on the BlueField?", "where do I drop the pod-spec YAML?", "pod stuck in Pending / ImagePullBackOff / CrashLoopBackOff", "container Running but service isn't ready", "pod restart-loops after edit", or "DMS and Firefly together", even without naming container deployment. A DOCA-linked binary launched directly on the host or Arm belongs to doca-bare-metal-deployment. Covers smoke-before-bulk and the layered error taxonomy (pod-spec, scheduling, image pull, runtime, mount, network, version, host). Refuse and route elsewhere for per-service config schemas, DOCA install, library-API questions, external NVIDIA services (BlueMan, HBN, SNAP, Virtio-net), or full Kubernetes-cluster ops.
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 DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline', 'gst-launch pipeline', 'process video with detection', 'build a pipeline', or any request involving GStreamer/DeepStream elements (nvinfer, nvstreammux, nvtracker, etc.).
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。