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 natea/fitfinder --skill monetization-analyzergit clone --depth 1 https://github.com/natea/fitfinderWrote 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/natea/fitfinder/monetization-analyzer)<a href="https://agentmods.dev/skills/natea/fitfinder/monetization-analyzer"><img src="https://agentmods.dev/badge/skills/natea/fitfinder/monetization-analyzer.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.09109 |
| Opus 5 | $0.00019 | $0.04555 |
| Sonnet 5 | $0.00008 | $0.01822 |
| Haiku 4.5 | $0.00004 | $0.00911 |
Grade A, and why
monetization-analyzer 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 4d 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 — 997 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monetization Analyzer Skill
Purpose
This skill evaluates game concepts to identify the most monetizable opportunities based on:
- Willingness-to-Pay (WTP) analysis from market data
- Viral potential and organic growth mechanics
- Revenue model optimization (premium, F2P, subscription, hybrid)
- Market demand and addressable market size
- Competitive pricing positioning
- Lifetime Value (LTV) projections
Output: Ranked list of top 3 most monetizable game concepts with detailed financial projections and go-to-market recommendations.
When to Use This Skill
Use this skill when you have:
- ✅ Multiple game concepts to evaluate for investment prioritization
- ✅ Market analysis data showing pricing sentiment and willingness-to-pay signals
- ✅ Need to identify which concepts have highest revenue potential
- ✅ Want to optimize monetization models before development
- ✅ Require financial projections for pitch decks or funding proposals
- ✅ Need to validate business model assumptions with market data
Prerequisites
Required Input Files
-
Market Analysis Report (from
market-analystskill)- Location:
/docs/market-analysis-*.md - Must include: Sentiment data on pricing, monetization pain points, willingness-to-pay signals
- Example:
market-analysis-fps-games-2025-10-26.md
- Location:
-
Game Concepts Document (from brainstorming/design)
- Location:
/docs/*-game-concepts-*.mdor/docs/plans/*-design.md - Must include: Price points, target personas, distribution channels, competitors
- Example:
fps-game-concepts-market-driven-2025-10-26.md
- Location:
Optional Input Files
- Competitor Financial Data (if available)
- Revenue reports, player counts, ARPU data
- Enhances accuracy of projections
Core Workflow
Phase 1: Data Extraction and Normalization
1. Load Market Analysis
Extract willingness-to-pay signals:
WTP_Signals = {
price_sentiment: {
"$0 (F2P)": {positive: X%, negative: Y%, mentions: N},
"$10-20": {positive: X%, negative: Y%, mentions: N},
"$20-30": {positive: X%, negative: Y%, mentions: N},
"$60-70": {positive: X%, negative: Y%, mentions: N},
"$70 + MTX": {positive: X%, negative: Y%, mentions: N}
},
monetization_pain_points: [
{issue: "Premium + battle pass", severity: "CRITICAL", mentions: N},
{issue: "Loot boxes", severity: "HIGH", mentions: N}
],
value_propositions: [
{model: "F2P cosmetic-only", sentiment: X%, examples: []},
{model: "Budget indie ($15-25)", sentiment: X%, examples: []}
]
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 997 lines · 39 tokens per session scan A ce02c8334cf7
monetization-analyzer is a skill published in the GitHub repository natea/fitfinder (4 stars, last pushed 10mo ago), licensed MIT. It adds 39 tokens to every session and 9,109 once invoked, about $0.0002 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 2026-09-03.
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