「without」の検索結果

Audit websites, apps, screenshots, mockups, and design code for harmful AI-design clichés, generic generated defaults, and established UI defects. Use when the user wants evidence-backed design feedback, an anti-slop review, or a removal-first cleanup plan without a speculative redesign.

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。

Apply exact Tailwind arbitrary shadow utilities for polished, layered neutral elevation. Use when compact cards, controls, panels, popovers, hero media, feature callouts, or modal-like containers need refined shadows without default Tailwind shadow scales or colored tinting.

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。

Add coordinated spring, summer, fall, and winter states to a 3D scene, blending foliage, sunlight, sky, ground materials, snow, and particles without rebuilding the world. Use for seasonal controls, architectural walkthroughs, and outdoor scenes with four-season transitions.

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 a website or digital experience against its supplied source references for originality and plagiarism risk. Use when Codex must compare current or historical site output with reference pages, capture packs, screenshots, copy, brands, numbers, images, assets, videos, layouts, motion, or code; raise evidence-backed red flags; distinguish common visual grammar from distinctive copying; and propose concrete fixes without making unsupported legal claims.

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 multiple original brand campaign worlds from a supplied visual reference while controlling how close the new work feels without copying protected signature elements. Use when a user provides a brand identity image, poster, editorial campaign, moodboard, or generated concept and asks for inspired alternatives, several distinct brands in one visual family, a closer-to-reference V2, exact wordmarks inside images, or originality-safe image-generation prompts and outputs.

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 convincing timber for Three.js as one procedural MeshPhysicalMaterial with growth rings, cathedral figure, pores, medullary rays, checks, tool marks, end grain, and oil, wax, or lacquer finishes, so the grain follows the form and survives macro close-ups without image textures. Use for wood boards, benches, carved panels, tool handles, joinery, floors, and any request to make 3D wood look real, detailed, or less like a tiled photo or plastic.

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 sharp, physically coherent high-resolution materials for 3D rendering with appropriate PBR maps, texel density, UV direction, mipmaps, anisotropic filtering, and progressive asset delivery. Use for detailed wood, stone, fabric, foliage, and architectural close-ups without excessive download or GPU memory costs.

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, extend, or audit production-linked browser map editors for Three.js and isometric games. Use when Codex needs to create a private director view, derive a versioned editor document from authored placements, add outliner, layer, selection, drag, snap, inspector, or camera controls, expose enemy aggro, leash, or patrol overlays, implement local draft import, export, undo, and reset, secure an editor route with IP and password sessions, or validate and release a map editor without mutating live gameplay 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。

Upload one Kleros-ecosystem file per paid request to IPFS through the Kleros x402 gateway for $0.01 USDC on Base mainnet. Use for dispute evidence, MetaEvidence JSON, court/dispute/arbitrator policies, Curate item metadata, juror justifications, or any artifact a Kleros contract or subgraph will reference by CID. Trigger when the request mentions Kleros, court, arbitrator, dispute, juror, Curate, Proof of Humanity, evidence, meta-evidence, justification, explicitly names this gateway or skill, or asks to test or validate it. Do NOT use for generic IPFS/CID requests without Kleros context; recommend a general-purpose pinning service. Each request accepts exactly one file: different bytes require separate paid uploads, while identical bytes should reuse one CID.

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, deploy, and monitor a GenLayer Intelligent Oracle prediction market from any coding agent. Use when a user wants to create a settled-by-web-evidence prediction market without opening the web UI.

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。

Interact with Kleros Curate registries across Ethereum Mainnet, Gnosis Chain, and Sepolia. Use when the user mentions Curate, Light Curate/LightGeneralizedTCR/LGTCR, Stake Curate/PermanentGTCR/PGTCR, Scout, token-curated registry/TCR, token lists, address/CDN tags, Goldsky, MetaEvidence, Verify Your List, or Curate calls such as addItem, removeItem, challengeItem, challengeRequest, and fundAppeal. Covers registry discovery and queries; policy/schema inspection; submissions, removals, challenges, evidence, appeals, rewards, execution; factory deployment; and frontend verification on all three networks. Also trigger for registry/list operations paired with Kleros, arbitrator, dispute, juror, or PNK context, and whenever the user names or tests kleros-curate. Do NOT use for non-Kleros registries or generic IPFS uploads without Curate context; route standalone uploads to kleros-ipfs-upload.

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, preview, save, or validate Preline theme CSS using the bundled local generator in this skill. Use when the user asks to create a new Preline theme, match a brand color, translate a mood into a theme, generate light and dark theme CSS, preview theme output without writing files, or review and fix generated theme tokens. Do not use for generic non-Preline CSS work or arbitrary command execution.

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 isolated git worktrees for feature development without switching branches

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 helps users automatically extract channel-level and video detail data from a specific YouTube channel via BrowserAct API. Agent should proactively apply this skill when users express needs like extracting channel video data, getting latest or popular videos from a YouTube channel, tracking competitor channel content, extracting video metrics such as views likes comments, retrieving subscriber count and channel info, monitoring posting cadence of a YouTube channel, gathering video data for content strategy analysis, getting earliest videos of a YouTube creator, analyzing engagement signals across a full channel, and downloading structured YouTube video details without manual scraping.

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 helps users extract full article contents from WeChat using the BrowserAct API. The Agent should proactively apply this skill when users express needs like finding full WeChat articles for specific keywords, tracking WeChat public accounts for industry trends, extracting WeChat article contents for media research, monitoring public relations on WeChat platforms, collecting competitor updates from WeChat, getting full article body from WeChat links, monitoring brand exposure on WeChat articles, retrieving structured WeChat data for sentiment analysis, summarizing daily news from WeChat, getting author and publication date for WeChat articles, or automating WeChat content extraction without scraping.

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 helps users automatically extract structured data from YouTube search results using the BrowserAct API. The Agent should proactively apply this skill when users express needs like searching for YouTube videos by keywords, finding the latest YouTube Shorts for a specific topic, gathering YouTube channel data for competitor analysis, monitoring trending YouTube playlists, extracting YouTube search results for market research, tracking view counts for specific YouTube keywords, compiling a list of YouTube videos on a subject, discovering new YouTube content creators in a niche, searching YouTube for tutorial videos automatically, and retrieving structured YouTube search data without opening video pages.

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 helps users extract structured video list data and comment data from YouTube using the BrowserAct API. The Agent should proactively apply this skill when users request searching for YouTube videos and their comments, analyzing viewer sentiment for a specific video topic, gathering audience feedback on AI or automation, extracting a list of top videos and their viewer reactions, compiling YouTube video data along with user opinions, retrieving competitor video titles and related audience discussions, monitoring public response to specific YouTube search keywords, summarizing comments from search results for market research, tracking viewer engagement metrics and replies for trending topics, collecting YouTube video URLs and author details alongside community discussions, or automating the extraction of YouTube comments without manual scraping.

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 helps users automatically extract YouTube video transcripts and metadata in batch via the BrowserAct API. The Agent should proactively apply this skill when users express needs like batch extract full transcripts from YouTube videos for specific keywords, scrape YouTube subtitles for a list of videos, get batch video metadata and likes counts for analysis, automate YouTube search and subtitle extraction, collect multiple video transcripts published this week, download bulk YouTube video subtitles without writing crawler scripts, build a dataset of transcripts from top YouTube videos, extract YouTube video URLs and publisher info in batch, gather full video content for AI summarization pipelines, monitor recent YouTube videos and extract their transcripts, batch retrieve structured subtitle data for media research, extract transcripts from trending YouTube content automatically.

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 helps users automatically extract YouTube video transcripts and metadata via the BrowserAct API. The Agent should proactively apply this skill when users express needs like extracting full transcript from a specific YouTube video, getting subtitles and metadata for video content analysis, gathering video titles and likes counts, summarizing YouTube videos without watching them, collecting channel details from a video URL, tracking transcript automation for specific videos, scraping YouTube subtitles for internal knowledge bases, fetching full video content for AI summarization pipelines, downloading structured transcripts from YouTube links, analyzing video text content for media research, monitoring video publisher information and channel links, or building datasets from YouTube video transcripts.

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 helps users automatically extract structured image data from Google Images via BrowserAct API. Agent should proactively apply this skill when users express needs like finding images for specific keywords, gathering product style images for competitors, building visual datasets at scale, scanning visual search results for market research, tracking localized image trends by country, compiling related image thumbnails and links, extracting image titles and source logos, fetching click through URLs from image results, monitoring competitor visual assets, sourcing creative content for specific topics, looking up product pictures in different regions, collecting structured image metadata without opening detail pages.

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 helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a competitive product, tracking sentiment of recent Amazon reviews, extracting verified purchase reviews for quality assessment, summarizing user experiences from Amazon product pages, monitoring product performance through customer reviews, collecting reviewer profiles and links for market research, gathering review titles and descriptions for content analysis, scraping Amazon reviews without requiring a login.

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。

Four-phase debugging methodology with root cause analysis. Use when investigating bugs, fixing test failures, or troubleshooting unexpected behavior. Emphasizes NO FIXES WITHOUT ROOT CAUSE FIRST.

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。

Sync delta specs from a change to main specs. Use when the user wants to update main specs with changes from a delta spec, without archiving the change.

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。

Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.

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。