Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "replication lag", "partitioning strategy", "consistency vs availability", or "stream processing". Covers data models, batch/stream processing, and distributed consensus.
From atum-systemnpx claudepluginhub arnwaldn/atum-system --plugin atum-systemThis skill uses the workspace's default tool permissions.
Provides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Fetches up-to-date documentation from Context7 for libraries and frameworks like React, Next.js, Prisma. Use for setup questions, API references, and code examples.
Integrates PayPal payments with express checkout, subscriptions, refunds, and IPN. Includes JS SDK for frontend buttons and Python REST API for backend capture.
A principled approach to building reliable, scalable, and maintainable data systems.
Data outlives code. Applications are rewritten, languages change, frameworks come and go -- but data persists for decades. Prioritize correctness, durability, and evolvability of the data layer.
Goal: 10/10. Rate data architectures 0-10 based on deliberate trade-off choices for data models, storage engines, replication, partitioning, transactions, and processing pipelines.
| Mistake | Fix |
|---|---|
| DB choice based on popularity | Match engine to read/write patterns |
| Ignoring replication lag | Read-your-writes consistency |
| Distributed txns everywhere | Single-partition + sagas |
| Hash partitioning everything | Key-range for time-series |
| Assuming serializable isolation | Check actual default |
| Conflating batch and stream | Match to data boundedness |