「keywords」の検索結果

Use when building a visual identity or brand kit with Scenario: a logo, wordmark, or icon as editable SVG, a color palette and typography spec, logo variants (mono, reversed, favicon), and brand applications like social templates, avatars, banners, or merchandise mockups. Also for a rebrand or refreshing an existing mark. Keywords: brandkit, brand kit, visual identity, logo, monogram, wordmark, SVG, vectorize, color palette, typography, brand guidelines, brand board, launch kit.

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 or editing images with OpenAI's GPT Image models on Scenario via MCP: text-to-image, edits from reference images, inpainting with an alpha mask, in-image text for logos and infographics, transparent cutouts, pixel sizing up to 4K, quality tiers up to xhigh and max, input fidelity for product or face detail, or choosing between Flare, Sunburst, GPT Image 2 and 1.5. Keywords: GPT Image 2.5, GPT Image 2, OpenAI, DALL-E, gpt-image, ChatGPT image, txt2img, img2img, background.

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 video needs its spoken words on screen through Scenario via MCP: burned-in styled captions for a TikTok, Reels, or Shorts cut, ad captions for sound-off feeds, YouTube subtitles, an SRT sidecar, transcription of a clip's audio, captions translated into another language, karaoke or word-by-word styles, or restyling and correcting an existing transcript. Keywords: captions, subtitles, SRT, transcribe, karaoke, word-by-word, burn in, closed captions, translate video, caption style.

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 or editing images with Seedream models on Scenario via MCP: text-to-image, image-to-image editing with reference images, posters or packaging with exact in-image text (non-Latin scripts too), subject-preserving edits, sets of related images in one run, editing a transparent asset without losing its alpha, or splitting an image into transparent PNG layers with Layerize. Keywords: Seedream 5.0 Pro, Lite, Flash, 4.5, Layerize, ByteDance, txt2img, img2img, layer 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。

Use when finished Scenario assets have to give something back: a caption for a dataset or alt text, a reusable style description, a verdict against a brief, a canny, depth, pose, or segmentation control map for the next model, or the asset itself found again by text, tags, or visual similarity and filed into a collection. Keywords: caption, describe, analyze, QA, control map, find similar, reverse image search, semantic search, tag, collection.

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 Scenario output must be checked and improved rather than accepted first roll: iterating a generation until it matches the brief, fixing a batch that came back off-brief, retrying failed shots methodically, wiring an automated generate, review, revise loop, or deciding whether to re-prompt, swap references, inpaint, post-process, or change model. Keywords: refine, iterate, critique, review loop, QA, self-correction, verify, acceptance criteria, retry, drift.

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 one approved visual, image or video, must ship at many sizes or placements through Scenario: adapting a master to 1:1, 4:5, 9:16, or 16:9, social formats for feeds and stories, YouTube thumbnails, storefront capsules and app store graphics, marketplace product images, link previews, display banners, a website hero, or choosing between crop, resize, expand, and generative reframe. Keywords: aspect ratio, resize, reframe, outpaint, expand, derivative, safe zone, thumbnail, banner.

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 or editing video with Luma Ray models on Scenario via MCP: cinematic text-to-video, image-to-video from a start frame, start plus end frame anchors, seamless looping clips, HDR output, restyling footage while preserving motion with edit strengths and face, pose, depth, or trajectory controls, reframing to another aspect ratio by outpainting, or budget prompt edits on real footage. Keywords: Luma, Dream Machine, Ray 3.2, Ray 3, Modify Video, Reframe, T2V, I2V, V2V, loop, HDR.

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 adding sound to a video or generating standalone audio with Sonilo models on Scenario via MCP: text-to-sound-effects, text-to-music, video-to-sound-effects or video-to-music from a clip, scoring silent AI video, foley, ambience, muxed video-to-video variants returning the clip with the new track mixed in, keeping speech while replacing music, or per-segment sound control. Keywords: Sonilo V1.1, SFX, soundtrack, score, keepSpeechVocal, segments, royalty-free.

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 or editing images with Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images with named roles, web search grounding for real-world subjects, rendering exact title text into posters, aspect ratio control, or choosing between Uni-1 Max and Uni-1. Keywords: Luma Labs, Uni-1, Photon, txt2img, img2img, image edit, reference images, webSearch, reasoning image model.

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 or editing video with MiniMax Hailuo models on Scenario via MCP: text-to-video, image-to-video, first and last frame anchors, reference images, videos, or audio, native stereo audio, bracketed camera commands, extending a clip, inserting a new scene into one, or recasting the people in it from photos. Keywords: MiniMax, Hailuo 3.0, H3, H3 Max, Turbo, Extend Video, Insert Video, Recast, Hailuo 2.3, T2V, I2V, V2V, 2K.

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 full paper details from a CNKI paper page including title, authors, affiliations, abstract, keywords, fund, classification. Use when the user needs detailed information about a specific paper.

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 advanced search on CNKI with field filters like author, title, journal, date range, source category (SCI/EI/CSSCI/北大核心). Use when user needs precise filtered search beyond simple keywords.

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。

Set up and run rank tracking using Ahrefs MCP: pick the right keywords to track, segment them by purpose, set baselines, and define alert thresholds. Use this skill when starting a new tracking project, baselining for a campaign, choosing what to monitor, or building a rank reporting cadence. Triggers on rank tracking, keyword tracking, monitor rankings, track positions, what should we track, ranking dashboard, baseline rankings, alert thresholds. Also triggers when a stakeholder wants weekly or monthly ranking visibility.

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。

Tailor a resume to a job description by extracting keywords, scoring match, and rewriting bullets for impact. Use when applying to a role, optimizing for ATS keyword match, or customizing a cover letter.

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。

Statute and regulation interpretation framework. Use when reading statutes, classifying requirements, analyzing operative keywords, applying canons of construction, or mapping compliance obligations from legislative text.

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。

One run from signal to decision. Finds the names of new AI models, products and repositories seen in the last 24 hours (X, Hugging Face, GitHub, sitemaps), then builds a keyword priority table for them, each new name and the generic search terms behind it, sized against Google Ads volume and Google Trends, with prioritize, watch or skip. Use when the user wants today's new AI keywords, an AI trend radar, or asks what new AI terms are worth building a page or tool for. Also takes names the user already has. Runs the new-term-radar command line; needs an AISA_API_KEY.

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。

When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages at scale," "directory pages," "location pages," "[keyword] + [city] pages," "comparison pages," "integration pages," "building many pages for SEO," "pSEO," "generate 100 pages," "data-driven pages," or "templated landing pages." Use this whenever someone wants to create many similar pages targeting different keywords or locations. For auditing existing SEO issues, see seo-audit. For content strategy planning, see content-strategy.

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。

When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.

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。

Expert blueprint for AI pathfinding (tower defense, RTS, stealth) using NavigationAgent2D/3D, NavigationServer, avoidance, and dynamic navigation mesh generation. Use when implementing enemy AI, NPC movement, or obstacle avoidance. Keywords NavigationAgent2D, NavigationRegion2D, pathfinding, NavigationServer, avoidance, baking, NavigationObstacle.

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。

Expert blueprint for turn-based combat with turn order, action points, phase management, and timeline systems for strategy/RPG games. Covers speed-based initiative, interrupts, and simultaneous turns. Use when implementing turn-based combat OR tactical systems. Keywords turn-based, initiative, action points, phase, round, turn order, combat.

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。

Expert blueprint for GPU particle systems (explosions, magic effects, weather, trails) using GPUParticles2D/3D, ParticleProcessMaterial, gradients, sub-emitters, and custom shaders. Use when creating VFX, environmental effects, or visual feedback. Keywords GPUParticles2D, ParticleProcessMaterial, emission_shape, color_ramp, sub_emitter, one_shot.

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。

Expert blueprint for TileMapLayer and TileSet systems for efficient 2D level design. Covers terrain autotiling, physics layers, custom data, navigation integration, and runtime manipulation. Use when building grid-based levels OR implementing destructible tiles. Keywords TileMapLayer, TileSet, terrain, autotiling, atlas, physics layer, custom 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。

Expert Godot shader patterns for batch-safe hitflash, alpha-scissor foliage/dissolve, screenspace postFX, depth reconstruction, triplanar, and instance uniforms — not first-shader tutorials. Trigger on draw-call batching breaks, discard vs depth-prepass, foliage shadows, post-process quads, or world-position FX. Keywords: instance uniform, ALPHA_SCISSOR, hint_screen_texture, hint_depth_texture, global uniform, sampler2DArray, canvas_item, spatial, post-processing.

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。