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replica-entrepreneur Checked

Researches the app being cloned and reads what its real users say in public reviews (App Store, G…
⚡ Productivity skills By Jakeschincariol Version v1.0.0 Added 2026-10-12 Updated 2026-10-12 Source ↗
9.1Overall rating
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0Downloads
1.9kStars

Installation

🤖 Install via AI

Copy the prompt below and send it to your AI assistant (e.g. Claude Code) — it will follow the instructions and install automatically.

Install the "replica-entrepreneur" skill by following the instructions at https://skill123.me/install/replica-entrepreneur.
⌨️ Command line install

Run in your terminal — downloads and installs to ~/.claude/skills/.

curl -fsSL https://skill123.me/install/replica-entrepreneur.sh | bash
📦 Download ZIP

Download the zip and extract it into your skills directory (e.g. ~/.claude/skills/), then restart your session.

⬇ Download v1.0.0 · 9 KB
📊 Static rating
9.1/10
Trigger
7.5
Structure
8.0
Workflow
9.6
Content
10.0
Engineering
10.0
Security
10.0

Six-dimension rubric with reviewer rationale.

View scorecard ↓
🛡 Dynamic test
—

Not live-tested yet — this skill is rated on static analysis only.

About this skill

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.

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

Documentation

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.

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
View full documentation

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.

Score breakdown

Trigger
7.5
What (research reviews, rank hates/missing/unsolved, fix plan, positioning) and When (eight quoted phrasings plus 'after /replica-diff') clear; rich trigger coverage including 'how do I make mine better' and 'make it sellable'; no explicit When-Not.
Structure
8.0
113-line SKILL.md plus reviews.py and themes.json; script and data file at skill root rather than scripts/ and references-or-assets (role mixing); no redundant docs; tool usage documented inline.
Workflow
9.6
Five ordered steps (collect -> rank -> three lists -> fix plan -> angle) with non-negotiable guards and a deterministic ranking tool designed against fabrication (drops linkless rows and counts them, dedupes, marks thin themes, warns under 30 reviews); freedom well split (script ranks, human reads unthemed negatives); verification via stated sample size and substring-only quotes; fix plan routes each item to the right sibling skill.
Content
10.0
Tight imperative prose; copyable commands with arguments; the positioning template block is directly fillable; source table with concrete query strings ('switched from X', 'X sucks').
Engineering
10.0
Valid frontmatter; script has shebang, argparse usage, stdlib only; themes.json referenced and present, default path resolved relative to the script; sibling skills referenced exist.
Security
10.0
Capability manifest: reads replica/reviews.csv and local themes.json, writes replica/feedback.md/fixes.md and features.csv rows; network access is agent browsing public review sites via declared official APIs (itunes.apple.com RSS, hn.algolia.com, Reddit API within terms) - exactly the declared scope; scraping against site terms is explicitly forbidden; no credential access, no persistence, no dynamic execution. Capabilities match declared scope.
Differentiation stage of the replica-* suite: collect real public reviews of the cloned app, rank complaints/requests/unsolved jobs with a stdlib Python ranker (reviews.py + editable themes.json), produce a fix plan and a positioning angle. Exceptional integrity guard rails: never fabricate, every quote verbatim and linked (script drops rows without URLs), reading-not-scraping within site terms, no fake reviews (FTC rule cited), reviewers' words are research not testimonials, thin themes and small samples flagged as such. Same structural nit as siblings: script and theme file at skill root instead of scripts//references/; no When-Not in description.