「reference」の検索結果

Compare UX patterns across multiple reference apps using pattern libraries produced by ux-extract. Reads 2+ pattern-library.md files, walks them category by category, identifies where apps converge (strong signal), where they diverge (genuine design choice), what's unique to one app, and what's absent across the set. Produces an opinionated comparison document with recommendations for a new build. No browser needed — pure markdown analysis. Trigger with 'compare UX patterns', 'how do top apps handle X', 'ux comparison', 'pattern comparison across reference apps'.

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Collect and analyze relevant papers, reports, and reference methods to inform method selection without fabricating references or copying models blindly.

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Format reference for Power BI Enhanced Report (PBIR) JSON schemas and patterns. Automatically invoke when the user asks about PBIR JSON structure, visual.json properties, PBIR expressions, objects vs visualContainerObjects, theme inheritance, conditional formatting patterns, extension measures, bookmarks, field references, filter formatting, query roles, PBIR page structure, report wallpaper, or any PBIR metadata format question.

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Use when generating or extending video with Google Veo models on Scenario via MCP: text-to-video, image-to-video from a first frame, first and last frame transitions, reference-to-video (R2V) with asset or style reference images, native audio with dialogue, sound effects, and ambience, negative prompts, seeded reruns, extending a 16:9 clip, or choosing a quality, fast, or lite tier. Keywords: Veo 3.1, Veo 3.1 Fast, Veo 3.1 Lite, Extend Video, Google, T2V, I2V, V2V, 720p, 1080p.

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Use when generating or editing video with Grok Imagine models on Scenario via MCP: text-to-video, image-to-video from a first frame, reference-to-video with @image tags for consistent people, products, or clothing, prompt-based editing, extending a clip from its last frame, native audio with lip-synced dialogue and sound effects, or choosing between first-frame and reference conditioning. Keywords: Grok Imagine Video 1.5, R2V, Grok Edit Video, Grok Extend Video, xAI, T2V, I2V, V2V.

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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.

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Use when generating or editing images with Scenario through MCP: text-to-image, image-to-image, instruction editing, inpainting or outpainting with a mask, background control, aspect ratio or resolution sizing, several outputs per run, or choosing between Scenario image models. Also when a run fails on prompt length, a plan-restricted model, or a reference image that was silently ignored. Keywords: txt2img, img2img, image edit, inpaint, mask, reference image, aspect ratio.

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Use when generating or editing images with Ideogram models on Scenario via MCP: posters, logos, menus, packaging, or signage with exact in-image text, editing an image in place while keeping its exact size, transparent PNG generation, background removal that keeps hair and glass edges, editable text layers for localization, or one consistent character from a reference. Keywords: Ideogram 4.5, Precise Edit, V4, V3, typography, aspect ratio, layerize, character reference, inpainting.

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Use when a creative task has no direction yet: finding inspiration, exploring looks or styles, proposing concept directions to pick between, building a moodboard or reference board, doing visual research, mining existing assets for a style, or escaping generic AI-looking output. Also when a saved Scenario collection should drive a new batch as art direction. Keywords: inspiration, moodboard, references, art direction, style exploration, concepts, serendipity, ideation, surprise me.

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Use when generating, extending, or editing video with Gemini Omni models on Scenario via MCP: text-to-video, image-to-video from a first frame, reference-to-video keeping a character, product, or place consistent, appending footage to a clip, restyling footage by prompt (season, wardrobe, art style), native audio with dialogue and ambience, or picking between first-frame and reference conditioning. Keywords: Gemini Omni Flash, 1.1 Flash, Google, T2V, I2V, V2V, extend, subject consistency.

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Build or audit a comprehensive brand style guide that documents the full brand system including story, logo system, color, typography, imagery, voice, applications, and dos/don'ts. Use this skill whenever the user wants to create brand guidelines, document an existing brand, build a brand book, audit an existing style guide for completeness, or produce the artifact that other teams will reference for years. Triggers on style guide, brand guidelines, brand book, brand standards, brand manual, style sheet, brand documentation, brand reference. Also triggers when the brand identity is finished and needs to be documented for handoff to designers, developers, vendors, or future team members.

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Produce a creative brief grounded in live reference sites and deliberately distinct from existing demos, for any new brand build. Composes with brand-archetype-system and creative-direction. Use this skill at the start of any brand or microsite build when the result needs to land in a specific aesthetic position and not drift into a default house style. Triggers on creative brief, design brief, build brief, brand direction, where should this brand sit, what should this site look like, reference sites for, archetype for, distinct from our other brands, divergence from prior, not the same as. Also triggers when prior builds have come out as siblings and a systematic divergence approach is needed.

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Convert authored markdown into a polished self-contained HTML document with a table of contents, numbered figures and tables, cross-references, footnotes, and print-ready CSS. Use when publishing a report, whitepaper, or memo.

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Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.

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Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.

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。

Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.

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Compares two or more band-tagged frameworks and produces a framework-neutral topic matrix showing coverage and gaps across all inputs, plus an optional reference-centric PLC crosswalk document when a reference framework is supplied.

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Generate a criterion-referenced rubric with descriptive performance levels for a task or objective. Use for marking guides and general curriculum contexts. For Manning programmes where Competent = success, use coherent-rubric-logic-builder instead.

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Verifies UK case-law citations, pinpoint references and quotations against the official public register — The National Archives' Find Case Law — and statutory references against legislation.gov.uk, before a document that cites them is relied on, served or filed. Every check returns a graded verdict: VERIFIED, MISMATCH (the citation resolves to a different case — the classic AI miscitation), NOT ON REGISTER, OUTSIDE COVERAGE (the register cannot answer — absence proves nothing), or UNCHECKABLE (a law-report citation). The grading exists because there are two ways to get this wrong: citing a case that does not exist, and accusing a real case of not existing. Use when asked to check citations, verify a case exists, confirm a quotation is verbatim, check a pinpoint paragraph, audit a draft's authorities, screen a document for hallucinated cases, or check a statutory provision is in force. Not a substitute for reading the judgment: existence is not authority.

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。

已有功能的需求变更闭环（门禁式七步）。触发硬条件：要改的功能已经实现并跑起来了。当用户说"改需求""需求变更""调整交互""改功能""重构流程"，或改动容易散到多个文件、碰到鉴权/存储/配置/权限、需要可靠验证 + 回滚方案时使用。流程：锁 scope 写 change brief → 从代码确认当前行为（不靠记忆和假设）→ 影响面与风险评估 + 回滚计划 → 提出新设计等用户批准（**未获批准绝不动代码**）→ 小而局部的 diff → 跑固定回归清单 → 更新文档和决策日志。产出：change brief + 代码 diff + 回归记录 + decision log。不适用于：全新功能从零做（走 issue-pool → page-solution-design（有界面时）→ backend-logic-design（有规则需讨论时）→ prd-test-writer）、只改视觉样式不动任何逻辑（按已有界面直接调整）、新想法还没定要不要做（先用 issue-pool 入池）、写 PRD/测试用例（用 prd-test-writer）、提交推送发版（用 git-push）。注意：本 skill 的七步流程通用于任何项目，但 scripts/impact_scan.sh 与 references/chrome-extension-regression-example.md 是 Chrome 扩展示例；通用回归使用 references/regression-checklist.md，按项目实际选择检查。

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Local user-profile maintenance skill for Codex, Claude Code, OpenClaw, OpenCode, and other agent harnesses. Use only when the user explicitly invokes $user-profile-keeper to create, initialize, update, query, correct, delete, export, or audit a local persistent user profile. It can extract durable collaboration preferences, requirement-expression habits, capability boundaries, recurring omissions, risk preferences, privacy boundaries, and typical events from the current session into auditable, confirmable, retractable local profile data. Do not auto-invoke, upload profile data, or replace task-clarifier's normal clarification flow.

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Use when the user asks about Moodboard, building a moodboard from reference images, curated moodboard presets, Soul Hex color transfer, applying a visual style direction to generations, or named moodboard styles like Y2K studio, Warm ambient, Theatrical light, Swag era, Flash editorial, Street photography, Asian nostalgia, Retro BW, Surreal solarization, etc.

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Use when the user wants a 3D model, mesh or GLB out of Higgsfield — image-to-3D, multi-view-to-3D, text-to-3D, rigging or animating a mesh, remesh / retopology, retexture, or 3D Body (human shape + pose from one photo) — or mentions 3D Jutsu / Scene Builder 3D (private Blender scene projects built through Python). Also use for a 3D turnaround of a character or prop as a consistent multi-angle reference, or a physical 3D blockout rendered as a staging reference for a video shot. Names that trigger it: generate_3d, sam_3_3d, image_to_3d, multi_image_to_3d, meshy_v7_image_to_3d, tripo_h3_1_image_to_3d, tripo_h3_1_multiview_to_3d, hunyuan3d_v3_image_to_3d, meshy_v6_text_to_3d, hunyuan3d_v3_1_text_to_3d, tripo_3d, 3d_rigging, meshy_v5_remesh, meshy_v5_retexture, sam_3_3d_body, animation_actions, scene_builder_3d_*.

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。

Controls facial expressions in Seedance 2.0 with FACS (Facial Action Coding System) Action Unit codes — muscle-level direction (AU12 = lip-corner puller, AU6 = cheek raiser) instead of emotion labels. Use whenever the user wants precise facial acting, a forced/uncanny/mixed expression, micro-performance in a close-up, monologue or dialogue facial beats, a 'which AU code for anger/fear/disgust' answer, or to generate a FACS reference sheet for a character. Pairs with higgsfield-soul Micro-Expressions (named expressions), higgsfield-audio (dialogue + lip-sync), and higgsfield-gpt-image-2 (the reference-sheet image).

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。