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概要と使いどころ
Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction.
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。
Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup
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 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.
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Explicit compatibility adapter for the collection's legacy PDF workflow and delivery conventions. Prefer the installed official PDF skill for generic PDF reading, generation, rendering and forms.
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。
GStack document generation method for turning implementation, product, or operational work into durable docs with clear audience and ownership.
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 cute 3D toy assets from photo-reference manifests. Use when Codex needs to turn scene photos into 2D toy concept prompts, choose a photo-to-3D route, produce or plan GLB assets for Three.js, or maintain a repeatable personal-gift/toy-character generation workflow.
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。
Validate and implement Sinclair ZX80 video emulation in the EMULATORS repository, including CPU-driven display generation, display-file execution, Z80 M1/refresh/HALT/INT interactions, character and inverse-video fetches, line counter and shift register behavior, horizontal and vertical sync, collapsed display files, PAL timing, flicker, and cassette/video coupling. Use when Codex reviews or edits namespace ZX81 code for the /mZX80 model, especially ULA, ULARegisters, MemoryVideoCode, Memory, PortManager, Screen, CPU interrupt timing, raster, or video tests.
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。
Validate and implement Sinclair ZX Spectrum 16K/48K ULA and board-level behavior in the EMULATORS repository, including PAL/NTSC raster timing, bitmap and attribute fetches, FLASH/BRIGHT/INK/PAPER rendering, CPU clock generation, lower-RAM and I/O contention, floating bus, snow effect, DRAM addressing and refresh, port FE keyboard/tape/border/sound behavior, interrupts, and ULA revision boundaries. Use when Codex reviews or edits namespace ZXSPECTRUM ULA, ULARegisters, Memory, PortManager, Screen, Sound, Datasette, Z80 bus timing, raster, contention, or ZX Spectrum hardware tests.
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。
Validate and implement Sinclair ZX81 video emulation in the EMULATORS repository for /mZX811, /mZX812, and /mZX813, including ULA character generation, Z80 display-file execution, M1/refresh/HALT/INT behavior, scanline timing, HSYNC/VSYNC, NMI generator and WAIT synchronization, SLOW and FAST modes, PAL/NTSC margins, collapsed display files, inverse video, high-resolution techniques, ROM-version interactions, and ULA revision boundaries. Use when Codex reviews or edits namespace ZX81 ULA, ULARegisters, MemoryVideoCode, Memory, PortManager, Screen, CPU interrupt timing, raster, ROM-specific video flow, or ZX81 video tests.
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。
[OPTIONAL] AI-powered visual generation using Google Nano Banana Pro. For user flows, architecture diagrams, mockups.
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 writing, editing, debugging, or reviewing dealer language code in btn, dlr, or script files. Provides the complete dealer DSL syntax reference for bridge hand generation constraints.
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。
Solves complex, open-ended problems using a knockout tournament mechanism. Generates an initial population of diverse candidate answers, pairs them into head-to-head matches, produces a hybrid "child" solution from each pair, and advances the best of the three through a bracket until a winner emerges. Use this skill whenever the user wants to competitively select the best answer from a field of alternatives, asks for a "tournament" or "bracket" style search, wants a faster alternative to full evolutionary search, or uses phrases like: "run a tournament on this", "bracket-style search", "let solutions compete", "knockout search", "find the best answer by elimination", or "which solution wins?". Also trigger when the problem is open-ended and the user wants a clear winner quickly rather than a deep multi-generation refinement.
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。
Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.
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 asking project status, next priority, sprint health, blockers, deadlines, risk register, or daily brief 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 to process one to 99 local exact 2:1 equirectangular panorama images with Realsee Argus, producing depth maps, a merged GLB point cloud, camera poses, optional intrinsics, and a validated local output index. Trigger for Argus panorama reconstruction, Argus ZIP input, or explicit Argus start/status/collect lifecycle requests. Do not trigger for panorama editing or stitching, arbitrary-photo 3D generation, existing GLB inspection, or research-only questions.
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。
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
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。
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
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。
Elite mobile app image-generation skill for creating premium, app-native screen concepts and flows. Designed for iOS, Android, and cross-platform mobile products. Prioritizes clean hierarchy, comfortably readable text, strong multi-screen consistency, controlled color palettes, non-generic creative direction, textured surfaces, image-led composition, tasteful custom iconography, and clean phone mockup framing. By default, screens should be shown inside a subtle premium iPhone or similar phone mockup with a visible frame, while the main focus stays on the app content itself. This skill generates images only. It does not write code.
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。
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 OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: "Azure OpenAI", "AzureOpenAIClient", "ChatClient", "chat completions .NET", "GPT-4", "embeddings", "DALL-E", "Whis...
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。
Scrape leads from multiple platforms using Apify Actors.
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 hand-drawn 16:9 article illustrations with the Grav character IP, sparse annotations, and absurd but clear visual metaphors.
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 AI videos with Google Veo, Seedance, Wan, Grok and 40+ models via inference.sh CLI. Models: Veo 3.1, Veo 3, Seedance 1.5 Pro, Wan 2.5, Grok Imagine Video, OmniHuman, Fabric, HunyuanVideo. Capabilities: text-to-video, image-to-video, lipsync, avatar animation, video upscaling, fole...
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。
AI voice generation, text-to-speech, and voice synthesis via inference.sh CLI. Models: Kokoro TTS, DIA, Chatterbox, Higgs, VibeVoice for natural speech. Capabilities: multiple voices, emotions, accents, long-form narration, conversation. Use for: voiceovers, audiobooks, podcasts, video nar...
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。