OTHER

Escalate Loom Standup Burnout Signals to a Manager

Analyzes each Loom standup transcript for burnout and at-risk language, and when a teammate crosses a risk threshold it pings the manager privately on Slack and raises…

CategoryOther
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Loom standup recording postedLoomLoom
  • ActionScore burnout risk + capture quotes with OpenAIOpenAI
  • LogicContinue only if risk crosses threshold
  • ActionSend private burnout signal DM to managerSlack
  • OutputOpen low-urgency PagerDuty check-in alertPagerDutyPagerDuty

What it does

Watches async standups for the human warning signs that get lost in a video feed: overwhelm, repeated overtime, frustration, or disengagement. When a teammate's standup crosses a defined risk threshold, it discreetly escalates to their manager so a real conversation can happen quickly.

When to use it

Use it when you care about catching burnout early and your standups are async, so the tone cues a manager would notice in person are invisible. Best for engineering or support teams where silent burnout shows up as a quiet Loom rather than a complaint.

How it works

  1. 1A new Loom standup recording triggers the workflow and the transcript is fetched.
  2. 2OpenAI scores the transcript on a burnout-risk rubric and returns the specific quotes that drove the score.
  3. 3A logic branch only continues when the risk score crosses the configured threshold; everything else is dropped.
  4. 4The manager gets a private Slack DM with the signal and supporting quotes.
  5. 5A low-urgency PagerDuty alert is opened so the check-in is tracked to resolution.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect LoomVideo transcripts, libraries.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Connect PagerDutyIncidents, on-call, escalations.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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