A four-file JSON prompt library landed on GitHub this week, and it does something most prompt collections never bother with: it checks its own work. The creator, a Redditor who goes by u/SnooDoubts4271, runs a paid prompt-generation SaaS called PromtExpress. What got open-sourced under MIT is the free half of that product: plain JSON prompt templates for text, code, image, video, audio, and music, no account or API key required.
Here’s the twist. Every template ships next to a zero-dependency validator that runs in CI. It checks that every {{placeholder}} in a template is actually declared, that every declared variable gets used somewhere, and that IDs match their file paths. Miss a variable, forget to close a placeholder, or let an ID drift from its filename, and the build fails before it reaches you. Most prompt libraries are just folders of text files you hope still work. This one catches its own rot.
The templates aren’t generic filler either. The mind behind it baked in four patterns worth stealing no matter which tool you use:
🔧 Grounding with source quotes. The meeting-notes template forces the model to cite the shortest phrase from your notes that backs each action item. No owner named? It writes “Unassigned.” No deadline? “No date.” The model is explicitly told never to guess.
🎨 Labeled hierarchy for image prompts. Exact rendered text goes in quotes under PRIMARY TEXT and SECONDARY TEXT labels, followed by a NEGATIVE block. The original poster says this noticeably cut down on garbled or extra text showing up in generated images.
🐛 “If you can’t name a failing input, it isn’t blocking.” The PR review template requires every blocking comment to come with a concrete scenario that actually breaks. Vague style nitpicks get filtered out on their own.
✅ Contract before code. The unit test template lists a function’s expected behavior and edge cases first. If the current code contradicts that contract, the template writes the test for the correct behavior and flags it “POSSIBLE BUG” instead of quietly locking the bug in as the spec.
Compare that to the usual GitHub prompt dump: a README, a folder of loose .txt files, maybe a star count as the only quality signal. No validation, no credit trail, no CI gate stopping a broken template from merging. This repo treats prompts like code that needs a build step, which is honestly overdue.
Want to try it yourself?
- Clone promtexpress-oss from GitHub.
- Pick one template that matches something you already do weekly. Meeting notes, PR review, and unit tests are the easiest wins.
- Swap in your own variables and run it once against your usual model.
- Compare the output against whatever prompt you normally use for that task.
- If it holds up, open a PR. The project needs translations into Turkish, German, and Spanish, plus new templates for flat-lay product photos, SQL from plain-language questions, support replies, radio ads, and podcast jingles. Your GitHub username lands in the authors field, credited on every file you touch.
One commenter already flagged a gap worth knowing before you build anything on top of this: the validator needs at least one test case designed to fail. Without that, a broken glob pattern can silently print “0 passed, 0 failed” and exit clean for weeks while nothing downstream actually gets checked. That’s a good instinct to steal for any CI setup, prompt validator or not.
Pro tip: don’t just copy the templates, copy the shape. The “source quote” grounding trick and the “name a failing input or it doesn’t count” rule work in almost any prompt you’re already running, JSON template or plain text typed into a chat window. Retrofit those two into your existing prompts today and you’ll likely see fewer hallucinated details and fewer vague complaints out of your AI reviewer.
Worth noting: the TypeScript and Python SDKs plus the CLI only matter if you’re using the paid PromtExpress API. Skip them entirely if you’re not. The free JSON library works standalone with any model you already have access to, which is the entire point of open-sourcing it in the first place. No lock-in, no forced signup, just templates you can drop into whatever tool you already pay for.
The one caveat worth flagging: this is still a young repo with a handful of upvotes and one open critique thread so far, not a battle-tested standard. The patterns hold up on paper, but the creator is explicitly asking which ones break on different models, so treat the four patterns above as a starting hypothesis, not gospel.
I was genuinely impressed that someone building a paid product gave away the exact patterns that make it work, and asked for critique instead of stars. That’s rarer than it should be.
Go grab the repo, run the validator against your own prompt folder, and see how many of your placeholders were quietly broken this whole time. 🚀
Frequently Asked Questions
Q: How does the validator prevent silent CI failures?
The validator includes a deliberately broken template that CI must reject, and throws a hard error if no templates are found. This catches situations where the glob pattern breaks silently but the check still reports success, a common trap in CI systems.
Q: What’s the “source quote” pattern and why does it matter?
Instead of letting the model guess, action items must cite the exact phrase from the source material. Missing fields become explicit (“Unassigned”, “No date”) rather than hallucinated, keeping outputs tied to real data.
Q: How does the image model text formatting pattern work?
Text is labeled explicitly as PRIMARY TEXT, SECONDARY TEXT, and NEGATIVE sections. This structured approach significantly reduces garbled text and hallucinated elements in image outputs.
Open-source prompt library: plain JSON templates, CI-validated, credited to their authors (looking for contributors + critique)
by u/SnooDoubts4271 in PromptEngineering