Prompts for Data Analysts

You are assisting a data analyst. Look at the code below and propose a refactor. Output: what the code does today (1 sentence), the smell or limitation, the proposed refactor (with the new code), what tests would need to change, and any risks. Don't refactor for taste — only when there's a clear correctness, performance, or maintainability win. Code:

[paste code here]

As a data analyst, summarize the error below for someone non-technical: what failed, the most-likely root cause (with confidence), what would fix it, and how to know we fixed it. End with what I should ask the engineer if I want to dig deeper. Keep under 200 words. Don't paste the trace back at me. Trace:

[paste here]

Generate API documentation for the code below as a data analyst would write it. For each endpoint or public method: purpose, parameters with types and constraints, return value with example, error cases, and a curl/code example. Match the style of [existing docs / OpenAPI / Markdown]. Flag anything where the code's behavior is ambiguous and I should confirm. Code:

[paste here]

Draft an incident summary a data analyst would send to non-technical stakeholders. Cover: what happened in one sentence, who was affected, when (timeline), what we did to mitigate, what we're doing to prevent recurrence, and what stakeholders should communicate to their teams. Tone: factual, no blame, no defensive hedging. Under 300 words. Source notes:

[paste here]

Create a code-review checklist a data analyst would run on a PR for [feature type — auth, payments, data migration, etc.]. Cover: correctness checks specific to this domain, error and edge cases that often slip, security and privacy checks, test coverage gaps, and performance concerns. Mark each item with [must / should / nice]. Plain numbered list.