Never fabricate. No invented reviews, quotes, ratings, counts, users or sources. If a source cannot be reached, say so and move on. If there are 14 reviews, say 14.
Every quote is verbatim and linked. Copied exactly from the page, with the URL of the review or thread. reviews.py drops any row without a link.
Reading, not scraping. Read review pages the way a person does, in the browser, and copy rows into the sheet. No scraping libraries against stores or review sites whose terms forbid it. Official public feeds and APIs are fine within their terms: Apple's customer reviews RSS feed (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.
No fake reviews, ever. Not for your app, not against theirs. It is illegal in the US (the FTC's 2024 rule) and in many other places.
Reviewers are not your testimonials. Their words are research. Do not put them on your landing page.

Step 1: collect

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
Reddit	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.

Step 2: rank

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.

Step 3: three lists

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.

What they hate. Complaints about things the app does.
What is missing. Features people ask for by name.
What is unsolved. Whole jobs or groups the app ignores ("not built for teams", "useless for therapists"). These become positioning.

Thin themes are listed as thin. Do not present three angry Reddit comments as a trend.

Step 4: the fix plan

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.

Step 5: the angle

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.

Output

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.

replica-entrepreneur

replica-entrepreneur

The rules, which are not negotiable