40+ ChatGPT & Claude prompts for Developers

**Role:** You are a staff software engineer with 12+ years across distributed systems, developer tooling, and large codebases. You read code the way a great editor reads prose — for clarity, blast radius, and the next person who has to touch it.

**Context:** You're reviewing a pull request. The author wrote: "[PR description]". Files changed: [list]. Total diff: +[X] / -[Y]. CI status: [passing/failing].

**Task:** Walk through the diff and produce a review that a junior engineer can learn from and a senior can act on.

1. Lead with the ONE thing that matters most: bug? performance? maintainability? security?
2. List "must-fix" items with specific file:line references and concrete suggested rewrites.
3. List "nice-to-fix" items separately — don't conflate.
4. Include one "food for thought" architectural observation if relevant.
5. Praise something specific (one thing) — name the file:line.

**Constraints:**
- NEVER nitpick style if a linter would catch it
- NEVER write "I'm not sure about this" — say what you'd verify instead
- Distinguish "this is a bug" from "this could become a bug"
- Cite specific lines: `src/foo.ts:42` not "in foo.ts"

**Output format:** Markdown review with 6 H2 sections — TL;DR, Must-fix, Nice-to-fix, Food for thought, Praise, Final recommendation (approve / request changes / comment-only).

I have a bug in my [language] code. Here is the error:
```
[error message + stack trace]
```
Here is the relevant code:
```
[code]
```
Walk through the bug: (1) Root cause analysis. (2) Why the error manifests here specifically. (3) All possible locations where this could originate. (4) The minimal fix. (5) The proper long-term fix. (6) How to test the fix. (7) How to prevent this class of bug in future.

Optimize this SQL query for performance:
```sql
[query]
```
Schema context: [table structures, approximate row counts, existing indexes]

Provide: (1) Explain plan analysis of the original. (2) Identified bottlenecks. (3) Optimized query with comments. (4) Index recommendations with CREATE INDEX statements. (5) Expected performance improvement. (6) Alternative approaches if applicable.