AI AGENTS
On-Demand HuggingFace Relicense Webhook to GitLab MR and Slack
Receives a webhook when a model repo event fires, verifies whether the affected pinned model's license or gating actually changed, opens a GitLab compliance MR.
How it runs
The automated pipeline, trigger to output.
- TriggerHuggingFace repo-change webhook receivedHTTP webhook
- ActionFetch current license and gating for the modelHugging Face
- LogicContinue only if changed from baseline
- ActionOpen GitLab compliance MRGitLab
- OutputPost change summary and MR link to SlackSlack
What it does
Reacts in near real time instead of waiting for a poll. When a HuggingFace repo-change webhook arrives for a model you track, the agent confirms the change is a real license or gating shift, opens a GitLab compliance MR, and pings the owning team in Slack with a one-line summary and the MR link.
When to use it
Use it when polling latency is unacceptable and you want compliance review to begin the moment a watched model changes. Best for teams with many pinned models where a daily sweep would react too slowly.
How it works
- 1A HuggingFace repo-change webhook hits the endpoint and triggers the flow.
- 2Look up the event's model id against your pinned set and fetch its current license and gating from HuggingFace.
- 3Branch: continue only if license or gating differs from the recorded baseline; otherwise acknowledge and stop.
- 4Open a GitLab MR updating the manifest, labeled `compliance-review`.
- 5Post a Slack message to the owning team with the change summary and MR link.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
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.
Datadog Bill Spike Attribution Agent
When a daily Datadog cost check detects a spend jump, an agent attributes the increase to the specific services and metric types driving it and posts a ranked breakdown to Slack.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
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.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.
