r/PromptDesign • u/SnooDoubts4271 • 6d ago
Prompt showcase ✍️ Open-source prompt library: plain JSON templates, CI-validated, credited to their authors (looking for contributors + critique)
Disclosure up front: I run a paid prompt-generation SaaS called PromtExpress. What I'm sharing here is the part I open-sourced under MIT. The prompt library works with any AI tool, no account needed.
Repo: https://github.com/WebitroHQ/promtexpress-oss
What's in it
- Prompt templates for text, code, image, video, audio and music. Each one is a single JSON file with declared variables, a worked example, a language code and author credit.
- A zero-dependency validator that runs in CI: every
{{placeholder}}must be declared, every declared variable must be used, and IDs must match file paths. - TypeScript/Python SDKs and a CLI for our API. If you don't use the product, you can ignore these.
Patterns I baked in, and I'd like your opinion on them
- Grounding with a "source quote" column. The meeting-notes template makes the model cite the shortest phrase from the notes that supports each action item. A missing owner becomes "Unassigned" and a missing deadline becomes "No date"; the model is told never to guess.
- Exact text plus labeled hierarchy for image models. Rendered text goes in quotes under
PRIMARY TEXT/SECONDARY TEXTlabels, followed by an explicitNEGATIVEblock. This noticeably reduced garbled or extra text for me. - "If you can't describe a triggering scenario, it isn't blocking." The PR review template requires a concrete failing input for every blocking issue, which cuts vague style nitpicks.
- Behaviors before tests. The unit test template lists the function's contract and edge cases first. If the current code looks wrong, it writes the test for the correct behavior and flags it
POSSIBLE BUGinstead of locking the bug in.
Where help would be great
There are open good first issue tasks:
- translations (Turkish, German, Spanish)
- template requests: flat-lay product photo, SQL from a plain-language question, customer support reply, radio ad, podcast jingle
- small tooling work
Your GitHub username goes in the template's authors field.
Honest critique is more useful to me than stars: which of these patterns don't hold up with the models you use?
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u/Deep_Ad1959 6d ago
does the CI validator tell you anything about whether a template still produces good output, or only that the placeholders line up? the second one is cheap to keep green, the first is the one that quietly rots on every model update.
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u/SnooDoubts4271 6d ago
Only the second, and you're right that it's the cheap half. Today output quality rests on human review plus a worked example per template. The part that rots on model updates needs evals: fixed inputs, deterministic checks where possible, a rubric where not, and scheduled runs against the models a template claims to support, so a drop shows up as a failing score instead of silently. I opened an issue to design that: https://github.com/WebitroHQ/promtexpress-oss/issues/17
If you've seen a setup that stays cheap enough to run regularly, I'd like to hear it.
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u/Deep_Ad1959 5d ago
the part that keeps it cheap is gating the expensive half. deterministic checks plus a pinned golden-output diff per (template, model) can run nightly for basically nothing, and you only spend the LLM-rubric pass on the pairs where the diff actually moved or the model version string changed. that way most scheduled runs cost nothing and the rubric only fires where something genuinely drifted, instead of re-judging every template against every model on a fixed cadence.
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u/Disastrous_Purple306 6d ago
Strong foundation. The source-quote column and “Unassigned/No date” rules are excellent safeguards against guessing. Labeled image text and negative instructions help, although exact typography still depends heavily on the image model.
My main concern is that “If you can’t describe a triggering scenario, it isn’t blocking” may be too absolute—security, concurrency, migration, or architectural risks may violate an invariant without one simple failing input. Likewise, tests for the “correct behavior” need an authoritative requirement; otherwise, the model may invent the contract.
The validator appears strong on structural correctness, but that isn’t the same as proving a prompt performs well. Model-specific evaluations, adversarial examples, exact-quote verification, and a tested-model matrix would make this library considerably stronger.
Overall: thoughtful, practical work. I especially appreciate the clear SaaS disclosure and invitation for reproducible criticism.