AI AGENTS
Conversational model-fit advisor
In a chat, an agent takes your task requirements, evaluates relevant HuggingFace models against them.
How it runs
The automated pipeline, trigger to output.
- TriggerChat message with requirements
- ActionSearch candidate modelsHugging Face
- ActionRead top candidate cardsHugging Face
- ActionWeigh candidates and pickOpenAI
- OutputReply with recommendation in chat
What it does
Acts as an on-call model-selection advisor in chat. You describe the task and constraints; the agent researches matching HuggingFace models, reasons about fit, and answers with a concrete recommendation, the runner-up, and why.
When to use it
Use for fast, interactive decisions — an engineer asking "what should I use for multilingual summarization under 3B params and a permissive license?" and wanting a defensible answer in seconds, with follow-up questions welcome.
How it works
- 1A chat message with the task requirements triggers the agent.
- 2The agent searches HuggingFace for candidate models that match the stated task and constraints.
- 3It reads the cards of the top candidates to gather license, size, and evaluation details.
- 4An LLM weighs the candidates against the requirements and selects a primary recommendation plus an alternative.
- 5The agent replies in chat with the pick, its tradeoffs, and a short usage snippet, ready for follow-up questions.
Set it up
What you configure once, before turning it on.
- 1Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 2Connect OpenAIModels, embeddings, files.
- 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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Resolved Incident to Public Troubleshooting Doc
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On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
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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