SOCIAL MEDIA

Harvest recurring Discord questions into a canonical Coda FAQ

On a schedule, pulls the last day of messages from your Discord support channels, clusters the recurring questions with an LLM.

CategorySocial Media
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule fires
  • ActionFetch last 24h of Discord support messagesDiscordDiscord
  • ActionCluster and rank recurring questions (OpenAI)OpenAI
  • LogicKeep frequent questions not already in FAQ
  • ActionDraft canonical answers (OpenAI)OpenAI
  • OutputAppend new Q&A rows to Coda FAQ docCodaCoda

What it does

This workflow turns the noisy stream of community questions in Discord into a clean, deduplicated FAQ table in Coda. Once a day it reads recent support-channel traffic, identifies which questions keep coming up, and adds the genuinely new ones to your canonical answers doc so the document stays current without manual triage.

When to use it

Run this when the same handful of questions get asked weekly and your team keeps re-answering them in chat. It is for community managers who maintain a public FAQ or help doc and want it to reflect what people are actually asking right now.

How it works

  1. 1A daily schedule fires the run.
  2. 2It fetches the last 24 hours of messages from the configured Discord support channels.
  3. 3An OpenAI step extracts question-shaped messages, groups near-duplicates, and ranks them by frequency.
  4. 4A logic step keeps only questions above a recurrence threshold and not already present in the Coda FAQ table.
  5. 5For each surviving question, OpenAI drafts a concise canonical answer.
  6. 6The new question-and-answer rows are appended to the Coda FAQ doc as the final output.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DiscordCommunity channels + voice + bots.
  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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