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概要と使いどころ
Provider-independent production guidance for AI agents creating or retouching appetizing food and beverage images, recipe videos, restaurant/menu visuals, CPG ads, ecommerce product images, beverage pours, packaging/lifestyle shots, and social food clips, including truthfulness, labeling/claims discipline, styling, prompt translation, platform variants, accessibility, rights, alcohol, and QA.
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。
Provider-independent production workflow for AI agents creating brand launch films, product reveal videos, manifesto films, campaign hero films, website hero videos, investor/customer launch assets, and social cutdowns. Use when planning, scripting, generating, editing, reviewing, or delivering launch films that must align strategy, claims, brand voice, visual direction, product proof, accessibility, compliance, and multi-platform delivery.
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。
Provider-independent music supervision and scoring direction for AI agents producing generated videos, ads, trailers, explainers, product films, social clips, documentaries, game trailers, and branded content. Use when planning, prompting, selecting, licensing, mixing, repairing, or quality-reviewing music, score, temp tracks, needle drops, AI-generated tracks, cue sheets, stems, loudness, loopability, platform music constraints, and rights-aware audio handoff for visual media.
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。
Provider-independent motion graphics direction for AI agents producing title sequences, lower thirds, explainer graphics, product UI overlays, social ads, kinetic typography, diagrams, data callouts, brand films, and video post. Use when planning, prompting, storyboarding, specifying, implementing, reviewing, or QAing motion graphics across live footage, generated video, UI capture, data visualization, brand systems, typography, accessibility, claim integrity, runtime handoff, and iteration.
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 this skill for provider-independent reality capture that turns photographed or video-derived real places, objects, aerial sites, interiors, or people into photogrammetric meshes, NeRFs, or 3D Gaussian splats. It guides representation choice, capture planning, calibration and scale, reconstruction or training, cleanup, compression and LOD, coordinate and interchange handoff, visual/geometric QA, archival records, and property, location, people, and cultural rights checks. Do not use it for text-to-3D generation, synthetic asset invention, guaranteed metrology, or broad downstream finishing after handoff.
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。
Provider-independent production workflow for AI agents creating educational animated lessons, classroom explainers, STEM visualizations, history/social-science animations, training modules, microlearning clips, whiteboard-style explainers, diagram-driven videos, and generated or assembled instructional animation assets. Use when planning, scripting, storyboarding, generating, reviewing, localizing, or QAing animation whose primary purpose is learning.
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 the user wants to design, rewrite, refine, or vary emotional acting prompts for live-action AI video characters. Translate abstract emotions, dialogue, subtext, and scene context into concise, observable, temporally ordered micro-performance directions. Preserve the user's existing character, scene, camera, and dialogue requirements unless they explicitly ask to change them. Do not use for general story writing, shot design, action choreography, or visual asset design when emotional performance is not the main task.
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 forest plots (HR, OR, RR, beta-coefficient summaries with CIs) and funnel plots (meta-analysis publication-bias diagnostics) using forestplot, metafor, ggforest, and MendelianRandomization with proper axis-scaling, summary-diamond placement, subgroup nesting, and Egger / trim-and-fill asymmetry tests. Use when summarizing effects across subgroups, trials, or instruments — meta-analysis, Mendelian randomization, subgroup HRs.
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。
Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.
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。
Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.
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。
Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and clustering on single-cell 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。
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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。
Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.
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。
End-to-end copy number variant detection workflow from BAM files. Covers CNVkit analysis for exome/targeted sequencing with visualization and annotation. Use when detecting copy number alterations from sequencing 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。
Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.
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 and read bigWig browser tracks for visualizing continuous genomic data. Convert bedGraph to bigWig, extract signal values, and generate coverage tracks using UCSC tools and pyBigWig. Use when preparing coverage tracks for genome browsers or extracting signal at specific regions.
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。
Genome-wide association studies (GWAS) with PLINK. Perform case-control and quantitative trait association testing using logistic/linear regression with covariates, generate Manhattan and QQ plots for result visualization. Use when running GWAS or association tests.
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, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq.
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。
Workflow for enrichment testing, ranked-gene analysis, pathway scoring, and pathway-focused visualization across omics 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。
Workflow for RNA modification analysis such as m6A peak calling, differential modification, and transcript-level visualization.
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。
Workflow for copy-number estimation, segmentation, annotation, and visualization in sequencing-based assays.
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。
Workflow for Hi-C and related 3D genomics analyses including compartments, loops, TADs, differential contacts, and visualization.
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。
Workflow for regulatory network inference, regulon scoring, perturbation-aware comparison, and network visualization.
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。
Capture high-quality screenshots of any public URL at custom resolutions. Supports desktop and mobile viewports, full-page captures, and specific element targeting. Ideal for visual monitoring, design research, and content archival.
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。