Competitor teardown
ASO & app-market research skills for AI agents — Claude Code, Cursor, and any MCP client. Install: npx skills add getappniche/aso-skills
npx -y skills add getappniche/aso-skills --skill competitor-teardownAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 12 days oldThe repository was created 12 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Deep-dive one or more named competitor apps — trajectory, monetization, positioning, and exploitable weaknesses. Use when the user names specific apps to analyze or asks "how is X doing" / "how do I beat X".
SKILL.md
2.9 KB, as published. Nobody here has run it
Competitor Teardown
Answer the question behind the question: not "what are X's numbers" but "where is X vulnerable and what should we copy vs. counter".
Requirements
GetAppNiche MCP connected: search_apps (to resolve names to ids), get_app_detail,
get_app_historicals, get_app_reviews. Canonical ids look like apple:284882215 /
google:com.example.app, but get_app_detail and get_app_reviews also accept a
bare Apple track id, an Android package name, or a store URL pasted from a browser —
so when the user hands you a link, use it directly instead of searching for the app.
Workflow
- Resolve and confirm. If the user gave a link or an id, skip straight to step 2.
Otherwise find each competitor with
search_apps(search_in: "title"narrows a name lookup); if multiple plausible matches, confirm with the user before spending credits on the wrong app. - Snapshot each app with
get_app_detail: downloads and revenue, rating and review count, price model, category, last update. - Pull trajectory with
get_app_historicals(90 days is a good default; up to 730 available). Look for: growth vs. plateau vs. decline, revenue moving independently of downloads (monetization change), rating drift (quality trend). - Read positioning from metadata. From each app's title/subtitle/description: who they target, the promise they lead with, keywords they visibly chase.
- Hear their users. Call
get_app_reviewswith the competitor'sapp_id— the live lookup works on any app in either store (Apple id or Android package name), no monitoring needed. One page of recent reviews turns "weaknesses worth exploiting" from inference into evidence: what their users actually complain about, and which complaints track the latest version. - Synthesize per competitor: two or three strengths worth copying and two or three weaknesses worth exploiting — each tied to a number, a quote, or an observed fact, not vibes.
- If the user has their own app, finish with a positioning gap: the audience or promise none of the competitors own, and the shortest credible path to owning it.
Output
Per competitor: a compact stat block (downloads · revenue · rating · trajectory arrow) plus the strengths/weaknesses lists. Then a comparison table if 2+ competitors, and the gap recommendation.
Guardrails
- Round figures for readability — say "~$40K/mo", never a false-precision figure.
- Historicals cost 1 credit per app; for a 5+ app comparison, snapshot all first and pull history only for the 2–3 that matter.
- A teardown without a recommendation is a report — always end with "so what".