This weekend a developer got tired of digging through Apple Notes for the same prompts. What they shipped is a searchable library of 33 battle-tested templates. The twist: these weren’t made for the public.
They were built to power Promta, a native macOS prompt manager the author is actively developing. Production prompts for a commercial product, open-sourced for free. That distinction matters more than it sounds. Most free prompt libraries are scraped from Reddit threads or generated in bulk to fill a download page. These ones had to work or the product breaks. When a developer bets their own app on a prompt, that prompt gets iterated until it delivers consistent output across edge cases. That’s a very different bar than “got 40 upvotes on r/ChatGPT.”
No signup. No email wall. Just raw Markdown you can copy. In a world where everything is gated behind a lead-gen form, that’s worth pausing on.
What you’re getting
33 templates across development, copywriting, and daily productivity. Each one was tested before it made the cut. Not tested in the sense of “seemed fine when I ran it once.” Tested in the sense of running it through a real workflow, repeatedly, until the output was consistent and the failure modes were patched out. Two standouts:
The Deep Dive Editor tells AI to strip buzzwords and focus on structural flow. It asks for the polished text plus exactly 3 key improvements. Clean output, no fluff. The “exactly 3” constraint is doing heavy lifting here. When you ask for “suggestions,” you get eleven bullet points covering everything from comma placement to audience tone. When you specify 3, the model has to decide which improvements actually matter. That forced prioritization is what makes the output usable instead of overwhelming.
The Strict Code Refactorer bans the textbook lecture. Returns only corrected code in fences. Nothing else. If the code is already fine, it returns the original unchanged. This solves one of the most frustrating LLM defaults: the urge to explain. Ask a model to fix a function and it will often give you three paragraphs about best practices before the corrected code even appears. This prompt kills that behavior at the source by making the constraint explicit in the system instruction rather than hoping the model figures it out.
The rest of the library covers meeting summary extraction, structured research breakdowns, email rewriting for tone shifts, a hooks generator for short-form content, and a prompt for converting passive sentences to active ones. The development category alone has prompts for code review, documentation generation, and step-by-step debugging walkthroughs. Most people will find at least five or six they can drop directly into their current workflow without any modification.
How to use this right now
- 🔍 Go to promta.app/prompts and browse by category. Spend two minutes scanning the full list before picking one so you understand the range of what’s available.
- 📋 Pick a template and copy the raw Markdown. The format is built for direct paste; brackets mark the parts you fill in, everything else stays as-is.
- ✏️ Paste into Claude or ChatGPT, fill in the bracket placeholder with specific context. Vague placeholder inputs produce vague outputs even with a tight prompt structure, so be precise here.
- 🔄 Run it and check the output against the format constraints. If the model ignores a constraint, prepend “Follow the format exactly” before your input. That phrase improves constraint adherence more reliably than repeating the constraint itself.
Pro tip
Don’t just copy these. Tear them apart. Every good prompt in this library has a constraint that removes a specific AI failure mode. The Code Refactorer bans meta-commentary because LLMs default to it. The Editor requests exactly 3 improvements because open-ended lists get bloated. Every constraint you see was added because someone ran the prompt without it and watched the output fall apart in a predictable way.
When you find a template you like, ask yourself what happens if you remove its most restrictive line. That answer reveals exactly what failure mode the author was solving for. Take the Strict Code Refactorer: pull out “return only corrected code in fences” and you get the standard wall of explanation back. Now you know the constraint’s job. With that understanding, you can write a version tailored to your stack, your style, and the edge cases specific to your project. A prompt adapted to your actual context will outperform the generic original every time.
Start with one template from a category you use daily. Adapt it. Run it twenty times. Refine it based on where it breaks. After two weeks you’ll have a small personal library that’s more useful than any public collection because it’s trained on your real use cases, not hypothetical ones.
Browse all 33 at promta.app/prompts 👉
I got tired of losing my tested prompts in Apple Notes, so I put my top 33 curated templates into a clean, searchable directory (Free, no email wall)
by u/Intrepid-Operation92 in ChatGPTPromptGenius