903건 · 관련도순
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create a delightful, unexpected "wow" experience for the user by dynamically discovering and creatively combining other enabled skills. Triggers when the user says "surprise me" or any request expressing a desire for an unexpected creative showcase. Also triggers when the user is bored, wants inspiration, or asks for "something interesting".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Material-3-derived dark festival system: deep charcoal surfaces, golden-yellow primary, cyan and lavender accents. Expressive, event-led, full tonal token set.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Print-inspired visual language for books, magazines, and reports with editorial grids and expressive typography.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Dynamic, vibrant style with thick borders, geometric shapes, high-contrast colors, and expressive typography conveying motion and vitality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Vibrant, personality-driven design with bold colors, playful graphics, and dynamic layouts that balance creativity with structure.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
High-contrast, expressive style with creative typography and bold color choices for visually striking interfaces.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Playful, character-driven design with expressive typography and bold graphics for landing pages and creative projects.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create and maintain comprehensive brand guidelines including visual identity, tone of voice, messaging, color palettes, typography, and usage rules for consistent brand expression. Use when establishing brand identity, creating style guides, defining visual standards, setting tone of voice, creating brand assets, or ensuring brand consistency.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create any visual design using Adobe Express templates — including flyers, posters, banners, social media posts (Instagram stories, Facebook posts, LinkedIn graphics), business cards, invitations, greeting cards, resumes, cover letters, brochures, newsletters, certificates, presentations, YouTube thumbnails, email headers, logos, menus, labels, and more. Use this skill whenever the user wants to make, design, create, or build any visual — even if they just say "make me a flyer", "design a poster", "I need something for Instagram", "create an event invite", "make a business card", or any similar request. Also handles requests to find or browse templates, edit text/copy, change background colors, or animate a design. Access: 🔐 Signed-In required | Gen AI: ❌
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create a delightful, unexpected "wow" experience for the user by dynamically discovering and creatively combining other enabled skills. Triggers when the user says "surprise me" or any request expressing a desire for an unexpected creative showcase. Also triggers when the user is bored, wants inspiration, or asks for "something interesting".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
These are instructions for creating design philosophies - aesthetic movements that are then EXPRESSED VISUALLY. Output only .md files, .pdf files, and .png files.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
You are a senior backend engineer operating production-grade services under strict architectural and reliability constraints. Use when routes, controllers, services, repositories, express middleware, or prisma database access.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tiers. Use when nominating vaccine targets, ranking neoantigens, or building a tumor-to-candidate pipeline. Binding details in mhc-binding-prediction; ranking in immunogenicity-scoring.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes. Covers the binding-affinity (BA) vs eluted-ligand (EL/presentation) distinction, why %Rank beats raw nM for cross-allele work, the MS abundance bias that misranks low-expression neoantigens, allele-coverage inequity, and length bias. Use when scanning a protein or peptide set for class I epitopes, scoring neoantigen candidates, or choosing a binding predictor. For CD4/HLA class II see mhc-class-ii-prediction.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.