Quantify game balance issues with data-driven rubric scores and next steps.
npx skills add https://github.com/fagemx/gstack-game --skill balance-review
Please help me install this Agent Skill. Skill: balance-review Source: https://github.com/fagemx/gstack-game/tree/main/skills/balance-review Command: npx skills add https://github.com/fagemx/gstack-game --skill balance-review
Balancing a game's economy is often based on intuition rather than data. This Skill provides a structured, repeatable framework to quantify faucet/sink balance, progression pacing, and monetization impact, enabling precise decisions and measurable improvements.
Run balance-review against your GDD or data, then review the resulting scores and recommended actions.
High-intent search queries and answers about installing and using this skill.
Game balance can be quantified by applying a data-driven rubric to faucet/sink balance, win rate distributions, and progression pacing, generating explicit numeric scores and actionable next steps from your provided data.
Evaluating a game economy model requires metrics covering faucet/sink balance, progression pacing, and monetization pressure. If specific data is missing, the analysis flags gaps and proposes concrete data collection steps to enable a robust re-analysis.
Scoring difficulty curves and character balance involves applying structured rubrics across six sections including Difficulty Curve, Character Balance, and Cross-Section Consistency, producing explicit numeric findings and risk flags to guide design reviews.
Yes, you can run a balance review with incomplete playtest data. The process identifies missing metrics, explicitly flags those data gaps, and proposes concrete data collection steps to ensure measurable improvements can still be planned.
Analyzing monetization pressure is best handled by applying a data-driven rubric to progression pacing and monetization signals, generating explicit risk flags and recommended next steps to guide live-ops decisions.
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