PERSONAL PRODUCTIVITY

Loom Standups into a Coda Tracker with Blocker Escalation

Transcribes each daily Loom update into a structured Coda standup table and flags any stated blockers to the channel so they get unblocked the same day.

CategoryPersonal Productivity
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled run after update window
  • ActionCollect Loom updates and fetch transcriptsLoomLoom
  • ActionParse transcript into done/next/blockers fieldsOpenAI
  • ActionAppend dated standup row per person to CodaCodaCoda
  • LogicDetect whether a blocker was stated
  • OutputEscalate blockers with owners to SlackSlack

What it does

This workflow turns spoken Loom standups into structured rows in a Coda doc. The agent parses each transcript into separate fields for yesterday's work, today's plan, and blockers, then appends a dated row per person. When someone explicitly says they're blocked, it escalates that item to a Slack channel so a teammate can clear it before the day is lost.

When to use it

Use it when leadership wants a queryable history of standups, not an ephemeral chat thread, and when blockers tend to sit unnoticed for days. Good for teams that report progress in Coda dashboards.

How it works

  1. 1A scheduled trigger runs after the daily update window closes.
  2. 2The flow collects the day's Loom updates and fetches each transcript.
  3. 3The agent extracts structured fields: done, next, blockers, and confidence.
  4. 4Each parsed update is written as a new dated row in the Coda standup table.
  5. 5A logic step checks whether any blocker text is present.
  6. 6If blockers exist, the flow posts them with owner mentions to Slack for same-day resolution.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect LoomVideo transcripts, libraries.
  2. 2
    Connect CodaDocs, packs, automations.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Connect OpenAIModels, embeddings, files.
  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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