Revenue benchmark

Skill getappniche/aso-skills/skills/revenue-benchmark

Benchmark revenue and monetization across a niche — what apps earn, which pricing models win, and what a realistic revenue target looks like. Use for "how much do apps like this make", pricing-model questions, and revenue potential sizing.From its SKILL.md

Install

npx -y skills add getappniche/aso-skills --skill revenue-benchmark

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

2.4 KB, 545 tokens by cl100k_base, as published. Nobody here has run it

Revenue Benchmark

Give the user a defensible revenue picture: the distribution, not just the outlier.

Requirements

GetAppNiche MCP connected: search_apps, get_app_detail, get_app_historicals.

Workflow

Define the comparison set. 10–20 apps that genuinely compete for the same user, via search_apps — a mix of leaders and mid-pack, not just the top 3. To get the spread rather than only the winners, run the query twice with min_revenue/max_revenue bands (e.g. above $50K/mo, then $5K–50K/mo) instead of taking the top N once; price_model (Free, Freemium, Paid) isolates a monetization approach when the user asks about one.
Collect revenue figures from search results, deepening the interesting ones with get_app_detail (price model, rating, review base).
Report the distribution, not the average. App revenue is power-law shaped: a mean is misleading. Show top / upper-middle / typical / floor tiers with example apps in each.
Connect model to outcome. Group the set by monetization approach (subscription, one-time paid, freemium/IAP, ad-supported if visible from price + revenue pattern) and note which tier each model concentrates in within this niche — priors from other niches don't transfer.
Check stability for the 2–3 benchmark apps that matter most: get_app_historicals (90–365d) to confirm the revenue isn't a one-off spike.
Answer the actual question. If the user is sizing their own opportunity: a realistic 12-month target sits near the typical tier, not the top — say that, with the assumptions (market, pricing, differentiation) that could move it.

Output

A tiered table — Tier · Revenue/mo range · Example apps · Dominant model — followed by 3–4 sentences of interpretation and, when relevant, the realistic-target paragraph.

Guardrails

Frame ranges ("roughly $10–50K/mo") rather than single exact points.
Never project the niche's #1 as the user's expected outcome.
If the niche's revenue is concentrated in 1–2 apps, that concentration IS the finding — flag winner-take-most dynamics explicitly.

What ships with it

Read from the repository

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