SOCIAL MEDIA

Brand mention triage: turn product feedback posts into Linear issues

Finds social posts where users request features or report bugs about your product, dedupes them against existing tickets, and files clean.

CategorySocial Media
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled scrape of brand mentionsApify
  • ActionClassify post as feature, bug, or noiseOpenAI
  • LogicRoute only actionable feedback forward
  • ActionMatch against open Linear issuesLinearLinear
  • OutputCreate deduped Linear issue with label and sourceLinearLinear

What it does

Mines your brand mentions for genuine product signal: feature requests, bug reports, and confusion about how something works. It strips out the noise, checks whether the issue already exists, and creates a well-structured Linear ticket with the original quote, a summary, and a suggested label so product gets a steady stream of real user input.

When to use it

When valuable product feedback is buried in social chatter and never makes it to the backlog. Use this to convert public complaints and wishes into trackable work without a human triaging every post by hand.

How it works

  1. 1A scheduled scrape gathers recent brand mentions via Apify.
  2. 2An LLM classifies each post as feature request, bug, or neither, discarding non-actionable posts.
  3. 3A branch routes only actionable feedback forward.
  4. 4The post is matched against open Linear issues to avoid duplicates; near-matches are added as comments instead.
  5. 5New, unique feedback becomes a Linear issue with summary, source link, and a suggested label and priority.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ApifyActors, scrapers, datasets.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  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.

Run this workflow in your colony.

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