MARKET RESEARCH
Turn feature-request reviews into triaged Linear issues
Mines app-store reviews with Apify, uses HuggingFace to detect which ones are feature requests, and files de-duplicated.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires
- ActionPull new reviewsApify
- ActionClassify intent, flag requestsHugging Face
- LogicDedupe against existing issues
- OutputFile or update Linear issuesLinear
What it does
This reads new app-store reviews, uses a HuggingFace classifier to separate genuine feature requests from praise, bugs, and noise, and creates a Linear issue for each distinct request, merging duplicates into the same issue with an upvote count.
When to use it
Use it when valuable feature ideas arrive in reviews but never make it into your tracker because the signal is drowned in star ratings and one-word comments. This routes only the actionable asks straight to engineering.
How it works
- 1A daily schedule triggers the run.
- 2An Apify actor pulls the day's new reviews.
- 3HuggingFace classifies each review's intent and flags feature requests.
- 4A logic step normalizes each request and matches it against existing Linear issues to avoid duplicates.
- 5New requests become Linear issues tagged `from-reviews`; recurring ones bump a request-count field and append the new quote.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 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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14-day trial. No DevOps. No Sales call. Provisioned in under a minute.