SUMMARIZATION
Escalate blockers from Loom standups to Slack
Transcribes each new Loom standup video, uses AI to detect blockers and dependencies.
How it runs
The automated pipeline, trigger to output.
- TriggerNew Loom standup video postedLoom
- ActionFetch video transcriptLoom
- ActionExtract blockers and likely ownersOpenAI
- LogicAny blockers found?
- OutputPost flagged blockers to SlackSlack
What it does
This workflow watches for new Loom standup videos, reads each transcript, and asks AI to pull out anything that sounds like a blocker, risk, or cross-team dependency. Instead of summarizing everything, it surfaces only the items that need action and posts them to Slack with a suggested owner so blockers get cleared the same day they're raised.
When to use it
Use this when standups are fine for status but blockers get buried in long videos nobody rewatches. It's a triage layer: keep the videos for context, but route the urgent parts to where your team actually responds.
How it works
- 1A new Loom video in the standup space triggers the run.
- 2The transcript is fetched from Loom.
- 3OpenAI classifies the content and extracts blockers, who's affected, and who likely owns the fix.
- 4A logic step checks whether any blockers were found; if none, the run ends silently.
- 5When blockers exist, a formatted message with each blocker and a suggested owner is posted to the Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect LoomVideo transcripts, libraries.
- 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 this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.