“composition”的搜索结果

Expert in ALL computational collage composition: photo mosaics, grid layouts, scrapbook/journal styles, magazine editorial, vision boards, mood boards, social media collages, memory walls, abstract/generative arrangements, and art-historical techniques (Hockney joiners, Dadaist photomontage, Surrealist assemblage, Rauschenberg combines). Masters edge-based assembly, Poisson blending, optimal transport color harmonization, and aesthetic optimization. Activate on 'collage', 'photo mosaic', 'grid layout', 'scrapbook', 'vision board', 'mood board', 'photo wall', 'magazine layout', 'Hockney', 'joiner', 'photomontage'. NOT for simple image editing (use native-app-designer), generating new images (use Stability AI), single photo enhancement (use photo-composition-critic), or basic image similarity search (use clip-aware-embeddings).

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Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.

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React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.

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Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.

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Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.

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Build and validate a static DAG reachability index from source-qualified path/chain decompositions and partial transitive-edge filtering. NOT for treating a partial edge filter as a complete transitive reduction or runtime scheduler.

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Validate finite-DAG order objectives and construct reachability-chain or direct-edge path partitions with explicit proof boundaries. NOT for equating reachability chains with direct-edge paths without checking the chosen objective.

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Expert in ALL computational collage composition: photo mosaics, grid layouts, scrapbook/journal styles, magazine editorial, vision boards, mood boards, social media collages, memory walls, abstract/generative arrangements, and art-historical techniques (Hockney joiners, Dadaist photomontage, Surrealist assemblage, Rauschenberg combines). Masters edge-based assembly, Poisson blending, optimal transport color harmonization, and aesthetic optimization. Activate on 'collage', 'photo mosaic', 'grid layout', 'scrapbook', 'vision board', 'mood board', 'photo wall', 'magazine layout', 'Hockney', 'joiner', 'photomontage'. NOT for simple image editing (use native-app-designer), generating new images (use Stability AI), single photo enhancement (use photo-composition-critic), or basic image similarity search (use clip-aware-embeddings).

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用Python实现文字生成图像、现有图像风格转换和指定区域补全，并指导如何控制构图与风格、调整生成参数及减少内存占用。

• 根据文字生成图像
• 将现有图像转为水彩风格
• 补全照片中的指定区域

使用pygount按语言统计文件数、代码行数和注释比例，了解开发项目的规模与构成。支持筛选语言、查看逐文件明细，并以表格或JSON输出。

• 了解项目的代码规模
• 确认使用语言和文件数
• 查看代码与注释的比例

Create a 16:9 animated hero composition with rotating rings, a globe or other focal graphic, and expressive typography. Deliver a single HTML document with CSS animations that a frame-capture exporter such as HyperFrames can turn into video.

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• Create an animated hero for a marketing page
• Design a title card with moving typography
• Build a looping poster with rotating rings and a globe

Create HTML-based videos with animated titles, synchronized captions, text-to-speech narration, scene transitions, and visuals that react to music. Define timing and layout, then check for overflowing content, readable contrast, and animation choreography.

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• Creating short product reveal videos
• Adding captions synchronized with audio
• Combining narration with animated text

Create generative art, or images made with programmed rules, using p5.js. Fixed random seeds make each rendering reproducible, supporting procedural posters, still images that suggest motion, and studies of artistic frame composition.

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• Posters generated with programmed rules
• Still images that suggest motion
• Studies of artistic frame composition

Create a three-scene video composition in a retro pixel style using a Hyperframes HTML template. Customize titles, labels, and colors while preserving scene transitions, short scene durations, and repeatable animation timing.

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• Create a retro pixel presentation video
• Customize a three-scene video template
• Preview scene transitions and motion in HTML

Create character, scene, and product images from structured descriptions of style, composition, lighting, and color. Guide generation with reference images and aspect ratio settings, using a Python script connected to a supported image generation service.

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• Creating characters with specified clothing, era, and appearance
• Visualizing scenes with a chosen mood, weather, and lighting
• Producing product images with specified materials and viewing angles

Design and test rules for activating skills through human requests, AI selection, applications, or other skills. Check eligibility before matching descriptions, then return a JSON decision showing the selected channel, score, and policy reason.

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• Separate explicit requests from automatic skill selection
• Test allowed callers and skill composition limits
• Check cases where no skill should activate

Routes video requests to the appropriate HyperFrames workflow based on the source material and intended result. It guides agents toward specialized steps for product promos, explainers, captions, motion graphics, music videos, and HTML-based video compositions.

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• Choose a workflow for a product launch video
• Turn an article or notes into an explainer video
• Add captions or graphic overlays to a talking-head video

Prepare voiceovers, background music, sound effects, transcripts, captions, and background cutouts for HyperFrames compositions. The skill guides audio provider selection, word timing, and caption styling, with local alternatives for several audio tasks.

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• Creating voiceovers for HyperFrames videos
• Preparing background music and sound effects
• Adding word-timed subtitles and karaoke captions

Plan the visual and narrative direction of HyperFrames videos, including colors, fonts, composition, narration, and scene rhythm. The skill helps apply existing design specifications, choose a style, and check whether the finished work follows that direction.

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• Match a video's appearance to existing design specifications
• Choose colors and fonts to suit a mood
• Plan scene rhythm and narration pacing

Mix audio already placed in HyperFrames compositions. Adjust fades, volume changes, and effects; reduce music frequencies that compete with speech; and apply shared processing to multiple tracks through a group with common volume controls.

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• Make narration easier to hear over background music
• Add fades and crossfades to placed audio
• Balance uneven voice levels and adjust tone

Guide the visual and storytelling choices for HyperFrames videos, including colors, fonts, composition, narration, and pacing. Use existing brand specifications or choose a visual style to give scenes a consistent look and rhythm.

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• Matching a video to existing brand guidelines
• Choosing colors and fonts for a video
• Planning the pacing of a multi-scene video

Bring Figma logos, brand colors, and components into HyperFrames as local assets and editable HTML. Reconstruct animation from storyboard states, with additional motion import options when a compatible connector or native export is available.

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• Import a Figma logo as a video asset
• Apply brand colors across imported components
• Turn a Figma frame into editable HTML

Add zooms, pans, masks, moving text, and depth to HyperFrames compositions. Build animation states that remain correct when jumping to any point in time, then inspect motion and final frames with diagnostics and snapshots.

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• Adding zooms and pans to a shot
• Revealing images with animated masks
• Animating text and SVG shapes

Guide HyperFrames video projects from setup and component selection through checks, previews, and rendering. Use command-line tools to inspect timelines, diagnose failures, compare frames, and choose local, batch, or cloud rendering for the finished composition.

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• Set up and preview a HyperFrames video project
• Diagnose build or rendering failures
• Render multiple videos from variable data