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/replica-entrepreneur

@77c9436
by Jake Schincarioljakeschincariol/replica-skill995 stars
123

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.

Requires /replica-launch /replica-brand
Use this Skill: https://skilld.dev/gh/jakeschincariol/replica-skill/replica-entrepreneur

Nothing lands on disk. Nothing to clean up.

Fork this Skill

Edit a local copy. It keeps the author and licence.

SKILL.md

≈153 tokens for metadata: the name and description. ≈1.1k when used: this file. ≈1.2k more on demand in 1 file.

Description uses 7.6% of example budget

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In our Claude Code example, all Skill names and descriptions share 8,000 characters. This Skill uses ≈612 characters, or 7.6%.

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Example settings and source

The example uses a 200k-token context and default Claude Code settings. The count includes the name, description, separators, and when_to_use when present. Codex also counts local file paths.

Skit's source and limits: Codex 0.160.1, Claude Code 2.1.292.

replica-entrepreneur

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

The rules, which are not negotiable

  • 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.

  1. What they hate. Complaints about things the app does.
  2. What is missing. Features people ask for by name.
  3. 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.

Source: SKILL.md on GitHub

Skill behaviors

skilld matched fixed text patterns in SKILL.md and file names. Patterns miss obfuscated code.

skilld run checks every file with the same patterns. It asks for approval before it loads a Skill with a behavior marked Needs approval.

Third-party checks

1 warning4d3 checks · Risk SAFE
  • Gen Agent Trust Hub4d

    The skill is designed to research app reviews from external sources to identify product gaps. It carries a low security risk related to indirect prompt injection, as it processes content from untrusted third-party websites that could contain malicious instructions. Additionally, it executes a local Python script to analyze data.

  • Socket4d

    No alerts

  • Snyk4d

    Risk: MEDIUM · 1 issue

Provenance

Signed by skilld at 77c9436. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 1 hour ago.

Activeupdated 5 days ago

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