SUMMARIZATION

Transcribe new Loom walkthroughs into a Coda spec rollup

When a new Loom recording finishes, it transcribes the walkthrough, summarizes it into a structured spec, and appends a row to a Coda rollup table with the summary, decisions…

CategorySummarization
Enginesim
Difficultybeginner
Triggerevent
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Loom recording finishesLoomLoom
  • ActionFetch Loom transcriptLoomLoom
  • ActionSummarize into spec schemaOpenAI
  • ActionAppend row to Coda spec rollupCodaCoda
  • OutputReturn new Coda row linkCodaCoda

What it does

Turns ad-hoc Loom product walkthroughs into a living spec table in Coda. Every recording becomes one structured row: a plain-English summary, the key decisions called out, open questions, and a deep link back to the source video and timestamps.

When to use it

Use it when your team narrates product or design decisions over Loom instead of writing them down, and those decisions keep getting lost. Good for PMs and design leads who want a searchable spec rollup without anyone manually re-watching clips.

How it works

  1. 1A new Loom recording finishing fires the trigger with its video ID and metadata.
  2. 2The flow pulls the Loom transcript for that video.
  3. 3OpenAI summarizes the transcript into a fixed schema: summary, decisions, open questions, owner.
  4. 4The structured fields are written as a new row in the Coda spec rollup table, with the Loom URL attached.
  5. 5The new row link is posted back so the recorder gets a confirmation.

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 CodaDocs, packs, automations.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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