skills/app-builder/templates/SKILL.md
Project scaffolding templates for new applications. Use when creating new projects from scratch. Contains 12 templates for various tech stacks.
npx skillsauth add legendaryabhi/agent-skills-hub templatesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Quick-start templates for scaffolding new projects.
Read ONLY the template matching user's project type!
| Template | Tech Stack | When to Use | |----------|------------|-------------| | nextjs-fullstack | Next.js + Prisma | Full-stack web app | | nextjs-saas | Next.js + Stripe | SaaS product | | nextjs-static | Next.js + Framer | Landing page | | express-api | Express + JWT | REST API | | python-fastapi | FastAPI | Python API | | react-native-app | Expo + Zustand | Mobile app | | flutter-app | Flutter + Riverpod | Cross-platform | | electron-desktop | Electron + React | Desktop app | | chrome-extension | Chrome MV3 | Browser extension | | cli-tool | Node.js + Commander | CLI app | | monorepo-turborepo | Turborepo + pnpm | Monorepo | | astro-static | Astro + MDX | Blog / Docs |
development
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.