Seven High‑Impact AI Prompts That Cut My Dev Toil (~30%)

This is a practical write‑up for other developers. No silver bullets, no magic—just a handful of tiny prompts that removed a chunk of repetitive work for me. Take what’s useful, ignore what’s not, and please adapt to your stack and constraints.

I’ll show you what I automated, how I kept it safe (guardrails), and where AI made things worse.

Baseline and Results (Honest, Rough Numbers)

Two weeks of normal work, timed with a simple timer and averaged. Your mileage will vary.

Task	Before	After	Savings
Writing clear PR descriptions	8–12 min/PR	2–4 min/PR	~6–8 min
Commit message polish (Conventional)	2–4 min/commit	< 1 min	~1–3 min
Test stubs for new code	10–20 min/file	3–7 min/file	~7–13 min
Docstrings / type hints	5–10 min/file	2–4 min/file	~3–6 min
Simple refactor planning	10–15 min	3–6 min	~7–9 min
Flaky test triage on CI runs	10–30 min/run	3–10 min/run	~7–20 min
SQL safety pass (LIMIT/params)	3–8 min/query	~2 min/query	~1–6 min

Over a week, this shaved off roughly 25–35% of the time I usually spend on glue work and polish. Not world‑changing, but very noticeable.

How to use these prompts in Cursor (or your IDE)

• Copy the prompt, then select the relevant context (diff, file, or log) in your editor and ask the model with the prompt.
• Favor short, structured outputs (bullets, headings, or diffs) so you can scan and decide quickly.
• When modifying code, request unified diffs or minimal edits. Never auto‑apply without review.
• Keep suggestions small. If the model wants to change a lot, ask it to limit to the highest‑impact 10–30 lines.
• In Cursor, allow the assistant to run read‑only git commands when asked (e.g., git merge-base, git diff). The prompts below include explicit steps.

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1) PR Description Generator (with Risks and Test Notes)

Generates a well‑structured PR description from your git diff, including risk areas, manual test notes, and any migrations to call out.

Prompt (the assistant should run the git commands to gather context):

Task: Generate a concise PR description from the current branch changes.

Steps to gather context (run in project root):
1) Determine base commit against mainline:
   BASE=$(git merge-base HEAD origin/main || git merge-base HEAD main)
2) Collect unified diff (exclude lockfiles):
   git diff --no-color --unified=0 "$BASE"..HEAD -- . ':!package-lock.json' ':!pnpm-lock.yaml' ':!yarn.lock'

Then, using only that diff, write a PR description with:
- What changed and why (plain English)
- Risk areas (files/concerns)
- Manual test checklist (steps)
- Rollback plan (1–2 lines)
- Breaking changes (if any)

Constraints:
- Use headings and bullet lists
- Keep under 250 words
- If migrations or schema changes are present, call them out explicitly

Usage tip: In Cursor, grant the assistant permission to run the commands above; then paste the result into your PR and tweak.

Guardrail: Read‑only suggestion—you still own the final wording.

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2) Commit Message Co‑Author (Conventional Commits)

Suggests a clean Conventional Commit subject/body based on staged changes.

Prompt (the assistant should run the git command to gather context):

Generate a Conventional Commit for the staged changes.
Output format:
<subject line>

<short body with key details and risks>

Rules:
- Subject: type(scope): imperative summary, <= 72 chars
- Include BREAKING CHANGE: section if applicable
- Keep body 2–5 short lines, bullets ok

Context to fetch (run):
git diff --cached --no-color --unified=0

Usage tip: Run this before committing; allow the assistant to execute the git command and paste the subject/body into your commit editor.

Guardrail: You always review and can edit the message.

---

3) Test Stub Generator (Edge‑Case First)

Creates a focused test skeleton from a target file, emphasizing edge cases and error paths. Works for JS/TS/PHP—adjust framework as needed.

Prompt (paste the source file after the prompt):

Generate a minimal but thorough test skeleton for the following file.
Prioritize edge cases, invalid inputs, and error paths. Use the idiomatic
test framework for the language (e.g., Jest/Vitest for TS, PHPUnit for PHP).
Output only code in a single block—no commentary.

<paste file contents here>

Usage tip: Create a new test file in your editor and ask the model to fill it using this prompt.

Guardrail: Keep generated tests small and readable; expand only after they pass.

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4) Docstring / Type‑Hint Suggester

Proposes docstrings and type hints for functions and methods.

Prompt (paste the file after the prompt):

Suggest concise docstrings and safe type hints for the code below.
Do not change logic. Call out any risky guesses. Output a unified diff
(---/+++ and @@) that adds only docstring/typing lines.

<paste file contents here>

Guardrail: Diff‑only output—apply selectively.

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5) Regex → AST Refactor Helper (Plan + Codemod Skeleton)

When a one‑off regex feels brittle, ask for a short plan for an AST‑based codemod (e.g., jscodeshift, php-parser), plus a minimal transform skeleton.

Prompt (edit the task line and include a tiny before/after example if you have one):

Help me replace a brittle regex refactor with an AST transform.

Task:
Rename a prop 'foo' to 'isFoo' in JSX where the value is boolean.

Output:
1) Brief risks and edge cases
2) Test cases (before/after)
3) Minimal codemod skeleton using a common tool (pick one), with TODOs
4) Rollback plan

Guardrail: Plan first; only then implement and run tests locally.

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6) Flaky Test Triage (Cluster and Summarize)

Clusters repeated failure signatures from CI logs and suggests next actions (retry, quarantine, fix owner).

Prompt (paste a trimmed CI log after the prompt):

From the CI log below, cluster failures by similar stack traces or error messages.
For each cluster, output:
- A short title
- The most representative snippet
- Count of occurrences
- Suggested next action: retry (<=2), quarantine, or assign owner (by path/module)

Then list the top 3 likely root causes across clusters.

<paste CI log here>

Guardrail: Keep logs small and anonymized; paste only the relevant failing parts.

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7) SQL Explainer + Safer Rewrite

Explains a query and proposes a safer version (parameters, LIMIT in dev, avoid SELECT *).

Prompt (paste the SQL after the prompt):

Explain what this SQL does and propose a safer version.
Prefer parameterized queries and add a sensible LIMIT for dev to avoid full scans.
Point out missing indexes if obvious. Keep the answer short.

<paste SQL here>

Guardrail: Suggestions only—you still implement parameterization in your code.

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Guardrails That Saved Me

• Dry‑run by default. Generate diffs/markdown—don’t mutate files silently.
• Keep patches small. If a suggestion touches > 30 lines, I stop and review manually.
• Allowlist paths. Never scan node_modules/, minified/binary files, or large lockfiles.
• Separate branches. Suggestions live on a feature branch; never commit to main.
• CI as the gate. Lint, typecheck, and tests must pass—no exceptions.
• Keep logs. Scripts print what they read and when, so I can audit later.
• Timebox. If a script struggles on a task for > 30 seconds, I bail.

When AI Hurt More Than Helped

• Greenfield design and naming. I prefer real domain conversations and a quick RFC.
• Performance‑sensitive code. LLMs hallucinate micro‑optimizations—benchmarks don’t.
• Ambiguous specs. The model will confidently pick the wrong path; talk to a human instead.

Wrap‑Up

None of these prompts are fancy, and that’s the point. They reduce friction, keep me moving, and stay out of the way. If you try one, start with the PR description generator or the test stubber—both are easy wins.

If you improve any of these, I’d love to see what you change. Happy building.