skills/brand-guidelines-anthropic/SKILL.md
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
npx skillsauth add legendaryabhi/agent-skills-hub brand-guidelinesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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To access Anthropic's official brand identity and style resources, use this skill.
Keywords: branding, corporate identity, visual identity, post-processing, styling, brand colors, typography, Anthropic brand, visual formatting, visual design
Main Colors:
#141413 - Primary text and dark backgrounds#faf9f5 - Light backgrounds and text on dark#b0aea5 - Secondary elements#e8e6dc - Subtle backgroundsAccent Colors:
#d97757 - Primary accent#6a9bcc - Secondary accent#788c5d - Tertiary accentdevelopment
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
development
Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and lending systems. Use when building decentralized finance applications or smart contract protocols.
tools
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
testing
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.