Research public reviews of an existing app to identify complaints, missing features, and underserved users. Rank findings by evidence, then turn them into a practical improvement plan and positioning options supported by linked, verbatim quotes.
Researches the app being cloned and reads what its real users say in public reviews (App Store, Google Play, G2, Capterra, Trustpilot, Reddit, Hacker News, its own feature-request board), then ranks what they hate, what is missing and what is unsolved, turns it into a fix plan for the clone and a positioning angle you can sell. Every quote is real, verbatim and linked. Use when the user says "what do people hate about X", "read the reviews", "how do I make mine better", "find the gap", "what features are missing", "how do I position this", "make it sellable", or after /replica-diff.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
A straight copy of an app has no reason to exist. This skill finds the reason: what the original's users hate, in their own words, and fixes it in yours.
Tool in this folder:
python3 reviews.py replica/reviews.csv --out replica/feedback.md
reviews.py drops any row without a link.https://itunes.apple.com/us/rss/customerreviews/id=<APP_ID>/sortBy=mostRecent/json),
the Hacker News Algolia API (hn.algolia.com/api/v1/search?query=...),
Reddit's official API under its terms.Aim for 100+ reviews across at least three sources, recent first:
| source | where |
|---|---|
| App Store | the app page, Ratings and Reviews, See All; or the RSS feed above |
| Google Play | the listing, See all reviews, sort by newest |
| G2, Capterra, Trustpilot | the product's review pages, filter to 1 to 3 stars too |
| search "X alternative", "switched from X", "X sucks", "X vs" | |
| Hacker News | the Algolia API or site search, same queries |
| the original's own board | its public roadmap or feature-request board (Canny and similar) and the vote counts |
| its changelog | what it shipped, so you do not "fix" what is already fixed |
Each row in replica/reviews.csv: source,url,date,rating,text, text copied
exactly. Read the 3 and 4 star reviews too. "Love it, but..." is where the
best fixes hide.
python3 reviews.py replica/reviews.csv --out replica/feedback.md
It sorts reviews into themes (themes.json, edit it for the app's category),
weights low ratings and recent reviews higher, marks themes with fewer than 3
reviews or only one source as thin, lists every request in the users' own
words, and surfaces low ratings that matched no theme. Read that last list by
hand. It is often the best part.
From feedback.md, write three ranked lists. Each item: the problem in one
line, how many reviews, how many sources, one or two linked quotes.
Thin themes are listed as thin. Do not present three angry Reddit comments as a trend.
Pick the top 5 to 8 by evidence times how cheaply you can fix them. For each:
what to build or change, size (S, M, L), which skill does it, and the
evidence. Add each one to replica/features.csv as a row with original set
to no. Pricing and billing complaints go to /replica-launch.
Three positioning options, each grounded in a top theme:
For {{who}} who {{hate this about the original, in plain words}},
{{your app}} {{does this instead}}.
Evidence: {{theme}}, {{n}} reviews across {{n}} sources.
Recommend one. It drives replica-brand's name and voice and replica-launch's hero. Do not put the original's name in your app name, ads or store listing. A factual comparison page is a legal question for a lawyer in your country.
replica/reviews.csv, replica/feedback.md, replica/fixes.md (three
lists, fix plan, angle), new rows in features.csv, and a summary that
states the sample size. Next: /replica-brand.
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