Getting an LLM to Make Me a Tool for Enriching My Icons' Color Metadata

An icon-gallery curator used an LLM to build a throwaway HTML tool that enriches color metadata across a collection of 2,000+ icons, rather than having the model tag icons blindly. The tool scores each PNG by the share of opaque pixels in each color bucket — skipping transparent pixels and low-saturation gray, converting to HSV, and measuring mass in hue bands such as roughly 15–45° for orange — then presents tagged versus possibly-missing icons per color with a threshold slider for visual review. The curator selects missed icons, copies their IDs back into the chat, and has the LLM write the metadata, a workflow the curator says would otherwise never have been done.

Getting an LLM to Make Me a Tool for Enriching the Color Metadata in My Icon Collection On my icon https://www.iosicongallery.com gallery sites https://www.macosicongallery.com , I have metadata I’ve manually added over the years to tag certain icons as being predominantly “blue” https://www.iosicongallery.com/colors/blue/ or “orange” https://www.iosicongallery.com/colors/orange/ or some other color. Then I use this metadata to present icons of roughly the same color. It’s kinda neat to be able to browse a wall of icons that are all the same color. The thing is: I know there are a lot of icons I’ve missed tagging over the years. But I have no idea how many, and figuring that out seems like a really arduous task. How do I go through 2,000+ icons and find all the ones that look predominantly “orange” but haven’t been tagged as such yet? Seems like a good task to throw at an LLM. But I don’t want to just say, “Go tag everything that’s missing” and blindly trust the output. I need to be able to make a decisions as to whether I think a particular color is “orange” or not. What I need is a tool for the job. I’m a very visual thinker, so to continue curating these color categorizations, I need some way for the computer to do its thing really, really fast, and then pull me into the loop to visually make decisions. Here’s how I am thinking about about accomplishing this task: - Create a page with a list of colors on the left red, green, orange, blue, etc. - When a color is clicked, show two columns. 1 All icons I currently have tagged for that color, and 2 all icons that might be that color but aren’t tagged as such you, computer, process all my icons and do the work to figure this out and make recommendations . - Allow me to select one or more icon s in the “not yet tagged as this color” column. Once I have all the ones I perceive as missing, give me a button to say “Copy” which copies the IDs of those icons. - I’ll paste the IDs back here in the chat and you go add the corresponding metadata. That seems like it would be a good tool to put me in control of visual decision making around color categorization. So I tell the LLM to run with it. We chat back and forth. I think, “You probably need to run all the icons through some model to make the correlation?” But it’s like, “Nah bro, just make a ‘hue histogram’.” It tells me how. For a color like “orange”, I can: process each PNG, skip transparent pixels, skip low-saturation gray, convert the rest to HSV, and score how much of the remaining mass sits in the orange hue band roughly 15–45° . Rank icons that don’t already have colorId: orange . Ok, sure. That sounds reasonable. This scores each icon PNG by share of opaque pixels per color bucket, then writes a standalone HTML page: tagged vs maybe-missing, per color. Let’s just make it, and then I’ll decide whether it’s good enough. After a few iterations, the computer going “brr…”, and me saying “explain that like I’m dumb”, I have a really effective little tool The little threshold slider is a nice touch. It lets me fiddle around with the fidelity of the matches. In some cases, sliding it down reveals more icons I would’ve otherwise missed. In other cases, I’m like “What are you thinking? I don’t see that as ‘yellow’ at all ” Supper effective little tool. I go through each color, select the ones I think are missing, paste the IDs back into the LLM, and then have it update each icon's metadata. Boom, done That all would’ve taken so long before. I would’ve never done it. Takeaways: - The LLM is good at making throw-away code. This doesn’t need to be “production-grade” code I depend on. Just something that’s good enough for me to get a job done, then toss. The resulting metadata is the goal, not the tool I use to get to the goal. - The LLM is good at making one-off HTML pages for a specific task. In my case, all these images were hosted on a CDN, easy enough to just point at and have a standalone .html file that I can locate on my hard drive and open directly as a file:// URL. No bundling. No transpilation. None of that. I don’t even need a web server Keeping things very basic on this project is paying off: I had really elemental building blocks that didn’t require additional third-party tooling. Just HTML, CSS, a little in-page JS, and images on a server - It’s fun to say, “Don’t do the work for me. Instead, help me make a custom-fit tool that facilitates me doing the work in the most empowering, correct way possible.”