AI AGENTS
Weekly Open-Model Scan -> Slack Swap Digest
Scans HuggingFace weekly for top trending models in your task category, runs each promising candidate against your fixed eval.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule starts the scan
- ActionPull trending + top open models in categoryHugging Face
- LogicFilter to compatible, maintained candidates
- ActionRun fixed eval on candidates vs incumbentShell
- LogicRank and label swap / watch / skip
- OutputPost ranked Slack swap digestSlack
What it does
Gives your team a weekly, evidence-based readout of whether a better open model exists for your task. It pulls the current trending and most-downloaded models in your category, benchmarks the credible ones against your incumbent on a frozen eval, and posts a single ranked Slack message with a clear swap/watch/skip call per candidate.
When to use it
Use it when model releases move fast and you want a recurring decision artifact instead of ad-hoc Slack links. Ideal for a model-ops or platform team that reviews the open-model landscape on a cadence.
How it works
- 1A weekly schedule starts the scan.
- 2The agent queries HuggingFace for trending and top-downloaded models in the configured task tag.
- 3A filter keeps only license-compatible, actively maintained candidates above a download/recency floor.
- 4It runs the fixed eval on each survivor and the incumbent, capturing score, latency, and cost deltas.
- 5A ranking step labels each as swap, watch, or skip with the deciding metric.
- 6It posts a formatted Slack digest with the ranked table and a one-line recommendation for the incumbent.
Set it up
What you configure once, before turning it on.
- 1Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 2Connect ShellRun sandboxed commands inside the workspace.
- 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.
More AI Agents workflows
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.
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.
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.
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.
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.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.