bikemeardsley/where-my-hug

Tell your AI agent what you want a model to do. It searches Hugging Face for
each part of the job and tells you which parts already exist there: which are
an exact fit, which to fine-tune, which to train, and which are just code.
Every model it names is real, current, and sized for your hardware.
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
- How it works: four questions ·
parts · the search ·
verdicts ·
the live check · flags ·
sizes
- Example: recipe cards
- Install (every agent, step by step: USAGE.md)
- What leaves your machine · Limits ·
Security
- Development · License
Try it
Add it to your agent (Claude Code here; every other option below):
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 on Hugging Face 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 Hugging Face 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:
| 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.
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. |