MARKET RESEARCH
Turn high-demand review requests into Linear tickets
On a manual run, finds the most-requested features hiding in your App Store reviews, drafts a structured ticket for each with user quotes and demand counts.
How it runs
The automated pipeline, trigger to output.
- TriggerOperator triggers run manually
- ActionScrape reviews over lookback windowApify
- LogicCluster requests, drop low-demand themes
- ActionDraft structured ticket bodiesOpenAI
- LogicDedupe against existing Linear issues
- OutputCreate labeled tickets in LinearLinear
What it does
This takes the recurring feature asks buried in your reviews and converts the strongest ones into ready-to-triage Linear issues — each with a problem statement, representative user quotes, demand count, and a suggested priority — so product can act instead of re-reading reviews.
When to use it
During roadmap or sprint planning, when you want raw user demand translated into the tracker your team actually works from. Run it on demand rather than on a schedule so you control when tickets land.
How it works
- 1You trigger the run manually, optionally passing a lookback window.
- 2Apify scrapes reviews for your app over that window.
- 3A logic step extracts and clusters explicit feature requests, dropping themes below a minimum mention count so only real demand gets a ticket.
- 4An OpenAI step writes each surviving theme into a structured ticket body with quotes and a suggested priority.
- 5A logic step checks for an existing matching Linear issue to avoid duplicates.
- 6New, non-duplicate tickets are created in Linear, labeled and prioritized, ready for triage.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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