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I built a tool to visually manage my LLM prompt templates and save them

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I built a tool to visually manage my LLM prompt templates and save them

This is Prompt Canvas - a simple, open-source web app that lets you visually build LLM prompt templates as YAML schemas and then generate complete prompts by populating values in the templates. It’s based on a single HTML file with no real privacy concerns and everything is portable thanks to YAML exports. Check out the example in the dropdown and read the guide to see how it works: https://promptcanvas.ml4den.com/ LLMs like structure and I found that generating prompts like this is an easy way of giving it to them. It can be useful if you’re doing a degree of prompt engineering and you want to test small variations in your prompts; or if you have a use case where you submit the same promps many times but with some input variations. I found that browsing to a YAML file and tweaking one parameter for a particular job is much cleaner than a web of Notion pages which is what I had before. Some thought and iteration has gone into the templating engine but everything is still early stage! Some of it is opinionated, and some of it is meant to be quite extensible to different use cases. Let me know if it makes sense. I built this mostly with Gemini 2.5 Pro out of my own necessity. Would love to know if it's useful for you! Feedback welcome; as are bugs and things on GitHub: https://github.com/ml4den/PromptCanvas

Visit productView on Hacker NewsSep 16, 2025
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Indie HackersFits the IH revenue-focused audience · Strong signals: para, gemini · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, single, gemini · Missing: mac, agents, macos
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Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
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Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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