software-design-system-design
SolidDesign scalable distributed systems using structured approaches for load balancing, caching, database scaling, and message queues. Use when the user mentions "system design", "scale this", "high availability", "rate limiter", "design a URL shortener", "system design interview", "capacity planning", or "distributed architecture". Also trigger when estimating infrastructure requirements, choosing between microservices and monoliths, or designing for millions of concurrent users. Covers common system designs and back-of-the-envelope estimation. For data fundamentals, see ddia-systems. For resilience, see release-it.
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Quality Score: 85/100
Skill Content
Details
- Author
- xiaohei-info
- Repository
- xiaohei-info/oh-my-multica
- Created
- 2 months ago
- Last Updated
- 4 days ago
- Language
- Python
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
ddia-systems
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.
system-design
Design systems, services, and architectures. Trigger with "design a system for", "how should we architect", "system design for", "what's the right architecture for", or when the user needs help with API design, data modeling, or service boundaries.
system-design
End-to-end system design for real codebases and greenfield problems. Use when the user asks to design or architect a system, map or reverse-engineer an existing architecture, review a design doc/PR/ADR, plan a migration, scaling evolution or service split, choose technologies (SQL vs NoSQL, Kafka vs SQS, cache strategy, sharding), size infrastructure (servers, QPS, storage, cost), or practice system design interviews. Also fires on oblique asks such as "is Postgres enough?", "will this hold at 10x?", "should we split this service?", "write an ADR", "capacity plan for peak", "our DB falls over at peak". Covers capacity math, failure-mode stress testing, right-sizing against over-engineering, cost estimation, distributed-systems fundamentals, and LLM-era infrastructure (RAG, vector search, model serving). Produces ADR-style design docs with Mermaid diagrams, architecture maps, scored reviews, and migration plans. NOT for UI/visual/CSS/logo design, single-query database tuning, or DevOps pipeline debugging.