skills/database-design/SKILL.md
Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.
npx skillsauth add legendaryabhi/agent-skills-hub database-designInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Learn to THINK, not copy SQL patterns.
Read ONLY files relevant to the request! Check the content map, find what you need.
| File | Description | When to Read |
|------|-------------|--------------|
| database-selection.md | PostgreSQL vs Neon vs Turso vs SQLite | Choosing database |
| orm-selection.md | Drizzle vs Prisma vs Kysely | Choosing ORM |
| schema-design.md | Normalization, PKs, relationships | Designing schema |
| indexing.md | Index types, composite indexes | Performance tuning |
| optimization.md | N+1, EXPLAIN ANALYZE | Query optimization |
| migrations.md | Safe migrations, serverless DBs | Schema changes |
Before designing schema:
❌ Default to PostgreSQL for simple apps (SQLite may suffice) ❌ Skip indexing ❌ Use SELECT * in production ❌ Store JSON when structured data is better ❌ Ignore N+1 queries
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.