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App discovery

Skill getappniche/aso-skills/skills/app-discovery

Scan an App Store or Google Play category or niche and produce a shortlist of apps worth studying, with revenue and download data. Use when the user wants to find apps in a space, size a niche, find underserved segments, or build a research candidate list.From its SKILL.md

Install
npx -y skills add getappniche/aso-skills --skill app-discovery

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 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.

SKILL.md

3.3 KB, 802 tokens by cl100k_base, as published. Nobody here has run it

App Discovery

Turn a vague market question ("what's out there in sleep tracking?") into a concrete, data-backed shortlist.

Requirements

Works best with the GetAppNiche MCP server connected (search_apps, get_app_detail). Without it, fall back to reasoning from public store pages and say so explicitly.

Workflow

  1. Frame the niche as filters, not just words. Restate the request as (a) a store — apple, google, or both; (b) categories to include; (c) a search phrase if the niche is narrower than a category; and (d) the thesis expressed numerically. search_apps takes the whole Explore filter set, so state the thesis directly instead of fetching broadly and filtering by eye:

    • "who makes real money here" → min_revenue: 10000, sort_by: "revenue"
    • "beatable incumbents" → min_revenue: 10000, max_reviews: 1000
    • "is this niche still being entered" → max_days_since_release: 365
    • "what's heating up" → growth_period: "30d", growth_direction: "up", min_growth_pct: 20, sort_by: "growth"
    • "abandoned apps to displace" → min_reviews: 1000, plus updated_before a year ago
    • "non-US opportunity" → languages: ["de", "ja"]

    Confirm only if genuinely ambiguous.

  2. Run two or three sharp queries, not ten scattershot ones. One query per thesis (limit: 25 is a good default). Because the filters do the narrowing, a second query should test a different hypothesis — not a synonym of the first.

  3. Deduplicate and rank. Merge results, then rank by whatever signal matches the user's goal. sort_by accepts downloads, revenue, growth, reviews, rating, title, released and updated, so let the query do the sorting.

  4. Deepen the top candidates. For the top 5–8 apps, call get_app_detail to pull ratings, review counts, and metadata before making claims about any single app. It accepts the canonical id (apple:284882215, google:com.example.app), a bare Apple track id, an Android package name, or a store URL pasted straight from a browser.

  5. Summarize the niche. Close with 2–3 sentences on the shape of the market: concentration (one winner vs. long tail), price patterns, and obvious gaps.

Output

A table — App · Store · Downloads/mo · Revenue/mo · Rating (count) · One-line angle — followed by the niche summary and a "worth a deeper look" pick with reasoning. State the filters you used, so the user can see what the shortlist is and isn't.

Guardrails

  • Use revenue and download figures to rank and compare; round them in output rather than quoting to the dollar.
  • Each call costs 1 credit. If a call reports an out-of-credits error, relay it: credits refresh monthly and can be topped up in Settings at app.getappniche.com.
  • A filter combination that returns nothing is a finding ("nobody is making $50K/mo here"), not a failure — report it before loosening the filters, and say which constraint you relaxed.
  • Don't invent apps to pad a thin niche — a short honest list is the finding.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.