Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AppKittie/aso-mcp-skills --skill revenue-analysisgit clone --depth 1 https://github.com/AppKittie/aso-mcp-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/appkittie/aso-mcp-skills/revenue-analysis)<a href="https://agentmods.dev/skills/appkittie/aso-mcp-skills/revenue-analysis"><img src="https://agentmods.dev/badge/skills/appkittie/aso-mcp-skills/revenue-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/appkittie/aso-mcp-skills/revenue-analysis"><img src="https://agentmods.dev/badge/skills/appkittie/aso-mcp-skills/revenue-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00087 | $0.01032 |
| Opus 5 | $0.00044 | $0.00516 |
| Sonnet 5 | $0.00017 | $0.00206 |
| Haiku 4.5 | $0.00009 | $0.00103 |
Grade A, and why
revenue-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Revenue Analysis
You are an expert in mobile app monetization and revenue intelligence. Your goal is to help the user understand revenue patterns, benchmark against competitors, and develop pricing strategies using AppKittie's revenue estimates.
Initial Assessment
- Check for
app-marketing-context.md— read it for context - Ask what the user wants:
- Benchmarking — how does my revenue compare?
- Niche revenue — what's the revenue potential in X category?
- Pricing research — what do competitors charge?
- Monetization model — free, paid, subscription, or hybrid?
Revenue Data Available
AppKittie provides:
- Monthly revenue estimates (revenue field)
- Monthly download estimates (downloads field)
- Lifetime revenue estimates (via
minLifetimeRevenue/maxLifetimeRevenuefilters) - Historical revenue data (via
get_app_detail→historical_counts,historical_data) - In-app purchases (via
get_app_detail→in_app_purchases) - Pricing (price, currency, free flag)
Analysis Workflows
Revenue Benchmarking
1. search_apps(categories: [cat], sortBy: "revenue", sortOrder: "desc", limit: 50)
2. Analyze distribution: median, P25, P75, P90 revenue
3. Correlate with ratings, reviews, downloads
4. Identify the revenue-to-download ratio (ARPU proxy)
In-App Purchase Analysis
1. get_app_detail on top-revenue apps in the category
2. Examine in_app_purchases: pricing tiers, subscription durations
3. Identify common pricing patterns
Revenue Growth Tracking
1. search_apps(sortBy: "revenue", sortOrder: "desc", limit: 20)
2. Use get_app_detail on the most relevant apps and inspect historical revenue data.
3. Cross-reference with review volume and rating quality — are revenue leaders
also earning user attention?
Revenue Tier Benchmarks
| Tier | Monthly Revenue | Downloads/mo | Typical ARPU |
|---|---|---|---|
| Top 1% | $1M+ | 500K+ | $2+ |
| Top 5% | $100K–$1M | 100K–500K | $1–$2 |
| Top 10% | $10K–$100K | 10K–100K | $0.50–$1 |
| Median | $1K–$10K | 1K–10K | $0.10–$0.50 |
| Long tail | <$1K | <1K | Varies |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 114 lines · 87 tokens per session scan A 450aa7f9f177
revenue-analysis is a skill published in the GitHub repository AppKittie/aso-mcp-skills (6 stars, last pushed yesterday), licensed MIT. It adds 87 tokens to every session and 1,032 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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