ENGINEERING
HuggingFace new-revision eval agent
Detects when a watched HuggingFace model publishes a new commit/revision, then runs an autonomous agent that reads the changelog, decides whether re-evaluation is warranted.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires
- ActionList new HuggingFace revisions since last seen SHAHugging Face
- LogicAgent judges cosmetic vs. substantive change
- ActionAgent drafts scoped eval plan for substantive changesHugging Face
- OutputFile eval plan as Linear issueLinear
What it does
Goes beyond field diffing: it watches the commit history of each model repo on HuggingFace and, when a new revision lands, hands the change to an agent. The agent reads the commit messages and updated card sections, judges whether the change is cosmetic (typo, badge) or substantive (new weights, changed chat template, safety note), and only then drafts a scoped eval plan as a Linear issue with concrete test suggestions.
When to use it
Use it when raw diff alerts are too noisy and you want a judgment layer that distinguishes "docs tweak" from "the model actually changed." Best for teams pinning models by revision who must decide whether to bump the pin.
How it works
- 1A schedule triggers the run.
- 2It lists recent HuggingFace commits for each watched model and detects revisions newer than the last seen SHA.
- 3The agent reads each new revision's diff and card changes.
- 4The agent decides cosmetic vs. substantive and, for substantive changes, drafts a targeted eval plan.
- 5It creates a Linear issue containing the plan, or logs a no-action note for cosmetic changes.
Set it up
What you configure once, before turning it on.
- 1Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 2Connect LinearIssues, projects, cycles, triage.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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