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Design scalable distributed systems using structured approaches for load balancing, caching, database scaling, and message queues.
Passed 21 of 22 test cases with the skill, and 18 of 22 without it.
and 5 more in the full report
3 fixed18 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
Here is my proposed architecture for an enterprise notification system: We plan to use an API gateway, Node.js worker nodes, and a PostgreSQL database. We expect heavy traffic during push marketing campaigns. Please review this architectural draft against the system design framework diagnostic. Provide an overall score out of 10, list which diagnostic checks failed, and outline concrete fixes for every failing item.
| Checked | Without skill | With skill |
|---|---|---|
| Evaluates the architecture using the 8 specific Quick Diagnostic rows (requirements listed, QPS/storage estimates, redundancy, DB scaling strategy, caching, async queues, monitoring/alerting, deployment strategy) | Missed | Passed |
| Calculates the numeric score out of 10 using the round(passed / 8 * 10) formula | Missed | Passed |
| Identifies missing QPS and storage estimates as a failing diagnostic row | Missed | Passed |
| Recommends adding a message queue like Kafka or SQS to decouple API traffic from worker nodes during campaign pushes | Passed | Passed |
The instructions your model reads when the skill loads.
A structured approach to designing large-scale distributed systems. Apply these principles when architecting new services, reviewing designs, estimating capacity, or preparing for system design discussions.
Start with requirements, not solutions. Jumping to architecture before understanding constraints produces over- or under-engineered systems. Scalable systems are assembled from well-understood building blocks (load balancers, caches, queues, databases, CDNs) — the skill lies in choosing the right blocks, sizing them with estimates, and owning the tradeoffs each choice introduces.
Goal: 10/10. Score a design by how many of the eight Quick Diagnostic rows it satisfies — score = round(passed / 8 × 10): 9-10 = all/nearly all rows pass — explicit requirements, real estimates, redundancy, a stated DB-scaling and caching strategy, async via queues, monitoring, and a deployment plan, with tradeoffs named; 5-6 = the design works but skips estimation, redundancy, or operations; <=3 = architecture proposed before requirements or estimates exist. Always state the current score, name the failing diagnostic rows, and give the specific fix for each.
Six areas for building reliable, scalable distributed systems:
Core concept: Every design follows four stages: (1) understand the problem and establish scope, (2) propose a high-level design and get buy-in, (3) dive deep into critical components, (4) wrap up with tradeoffs and future improvements.
Why it works: Without structure, designs either stay too abstract or get lost in premature detail. The four steps invest time proportionally — broad strokes first, depth where it matters.
Key insights:
Code applications:
| Context | Pattern | Example |
|---|---|---|
| New service kickoff | One-page design doc covering all four steps before coding | Requirements, API contract, data model, capacity estimate, then implementation |
| Architecture review | Walk reviewers through the steps sequentially | Scope, diagram, deep-dive on riskiest component, open questions |
| Incident postmortem | Trace the failure through the four-step lens | Which requirement was missed? Which block failed? What tradeoff bit us? |
See references/four-step-process.md when running a design end-to-end — per-stage time allocation, example clarifying questions, and tips for each of the four steps.
test cases passed, vs 18 / 22 without
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