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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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Create Python workflows for generating images from text, restyling existing pictures, and filling selected regions. Using HuggingFace Diffusers, the skill explains composition controls, style adapters, generation settings, and ways to reduce memory use.
Inspect a codebase with pygount to measure code lines, file counts, language composition, and comment ratios. Exclude dependency folders, focus on selected languages, and get summaries or file-level results for understanding project size.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Prepare Remotion Studio and Canvas Capture for a product demo recorded by the user. Import the original capture into packages/brand as an editable composition, preserve cursor metadata, and upload the verified recording to remotion.media.
Design and refactor Python components with clear responsibilities and simple structure. Assess when to split large classes, combine objects instead of extending them, and extract shared code without introducing unnecessary abstractions.