Tell your AI agent what you want a model to do. It searches Hugging Face,
Kaggle, Zenodo, ModelScope and Roboflow Universe for models and datasets for
each part of the job, and tells you which parts already exist: which are an
exact fit, which to fine-tune, which to train, and which are just code. One
model or dataset on several hubs is one finding, with every hub listed, so you
take it from the one you use. Every model and dataset it names is real,
checked live, and every model is sized for your hardware. It ends with where
to start.
Works with Claude Code, Codex, Copilot, Cursor, Gemini CLI, Cline, Devin, Open
WebUI and any other MCP client, or as an Agent Skill.
Its companion, Git for Me!, does
the same for open-source repos on GitHub, GitLab and Codeberg.
Contents
Try it
Add it to your agent (Claude Code here; every other option below):
claude mcp add --scope user where-my-hug -- npx -y where-my-hug
Then ask:
Where my hug at for transcribing handwritten recipe cards?
Your agent asks the few things it can't guess, splits the job into parts, and
comes back with a report like the one below, every
model and dataset in it looked up live moments before. It works for any job a
model might do:
Where my hug at for spotting which birds visit my feeder camera?
Where my hug at for sorting support emails by how urgent they are?
Where my hug at for pulling the totals off scanned receipts?
How it works
1. Four questions first
Ask for "a model that reads handwriting" and you get whatever is popular:
often too big for your GPU, under a license you can't ship, or in a format
your code can't load. So before anything is named, four details come first.
Your agent fills in what the conversation or your codebase already says, and
asks you the rest:
| What it asks | What that decides |
|---|
| Compute | Where will it run, and train if it needs to?one 24 GB GPU, CPU only, a phone, rented GPUs | How big a model can be, and whether fine-tuning is possible at all |
| License | What may the result be used for?commercial, open source, personal | Which licenses are allowed |
| Stack | What will call the model?a Python service, Node, the browser, a mobile app | Which weight formats work |
| Data | What training data is there, or could be?none, about 300 labeled photos, can collect more | Whether fine-tuning or training is realistic |
"Not sure yet" is a fine answer: the picks then cover the range, and every
model shows the memory it needs, so the report helps you decide.
2. Split the job
The agent breaks the need into parts, each with an input and an output
(photo → text, text → fields). A part that isn't a model's job (rules,
geometry, parsing, lookups) is marked WRITE: code does it exactly. Most
needs have one. A model doing work that code does exactly is a cost, not a
feature.
3. Search for each part
What an agent remembers of Hugging Face is as old as its training, and misses
the long tail. So nothing is picked from memory. For every other part, the
agent writes a search (the Hub's task tag, a few keywords, the job in a few
words), and it runs four ways on Hugging Face:
| Way | What it finds |
|---|
| Most downloaded for the task | What's proven |
| Trending for the task | What's new, including models released after the agent's training |
| Names holding each keyword | Fine-tunes and niche models |
| The text of model and dataset cards | The long tail, by what the card says, like a web search |
The ways take turns in the results, so a niche fine-tune isn't buried under
whatever is popular for the task.
Models and data live elsewhere too, so the same search covers four other
hubs, for models and datasets alike, taking turns the same way:
| Hub | What it adds | Key |
|---|
| Kaggle | Competition and community datasets, and Kaggle Models | none |
| Zenodo | Research datasets and models, with DOIs | none |
| ModelScope | Alibaba's hub: many Hugging Face models under the same names, and strong on Chinese-language data | none |
| Roboflow Universe | Labeled image sets for detection and segmentation, and models trained on them | ROBOFLOW_API_KEY, free |
ModelScope's search matches names, so it gets each keyword; the others get the
job in a few words. An id from one of them keeps its hub's name
(kaggle:landlord/handwriting-recognition, zenodo:1297399), so it's never
mistaken for a Hugging Face one.
The same model or dataset is often on several hubs: ModelScope carries many of
Hugging Face's models under the same owner and name. It's listed once, with the
others after "also on", and the report links each, so you take it from
whichever hub you use. Hugging Face's id leads where it has one, since it says
the most (monthly downloads, gating, who hosts it).
4. Decide each part from what turned up
| Verdict | Meaning |
|---|
| USE | A model the search found does this job as it is. |
| TUNE | One comes close: fine-tune it, ideally on a dataset the search found. |
| TRAIN | Nothing it found comes close: train a model, seeded by datasets it found. |
| WRITE | Not a model's job: write the code. |
This is the beginning of the README. Read the rest at the original. Read the full README ↗
