AI AGENTS
Weekly trending-model screen to Slack
Every week, an agent reviews newly trending HuggingFace models in your task areas, screens each card for adoption-worthiness.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule
- ActionFetch trending models by task tagHugging Face
- ActionRead each model cardHugging Face
- ActionScreen against adoption barOpenAI
- LogicKeep only qualifying models
- OutputPost digest to SlackSlack
What it does
Keeps your team current on the OSS model landscape without the noise. Once a week it pulls trending HuggingFace models in your focus areas, reads their cards, filters out the unfit, and shares a short digest of the few worth a closer look.
When to use it
Use when you want a low-effort radar on new releases relevant to your stack, surfacing only models that clear a quality bar — not a raw firehose of everything trending.
How it works
- 1A weekly schedule starts the run.
- 2The agent fetches currently trending HuggingFace models filtered to your task tags.
- 3It reads each model's card and metadata.
- 4An LLM screens every model against your bar (license, task fit, documentation quality) and keeps only those that pass.
- 5A filter drops the run quietly if nothing qualifies.
- 6The qualifying models are posted as a concise digest to a Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, 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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