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game-balance-economy

game-balance-economy

github.com/lvtd-llcCreative & media1152 installsUpdated 7w ago
Repository

About game-balance-economy

A workflow skill for designers tuning game numbers and economies. It starts from the intended player experience, examines resource flows and balance risks, then produces tuning and validation plans.

Key features

  1. 01Define the target experience
  2. 02Map resource sources and sinks
  3. 03Identify dominant strategies and imbalance
  4. 04Use expected value, simulation, and playtests
  5. 05Create tuning and validation plans

Use cases

  1. 01Tuning game currencies and rewards
  2. 02Reviewing odds, costs, and failure penalties
  3. 03Finding grind loops or scarcity collapse
  4. 04Planning balance tests and telemetry

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/lvtd-llc/skills --skill game-balance-economy

After installing, type/game-balance-economyin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/core/
guide.md
scripts/
expected_value.py
workflows/
tuning-pass.md

Details

From the project README

Game Balance Economy

Use this skill when a game system needs numerical tuning. Balance is not just fairness; it is the shape of player decisions over time. A good balance pass names the intended experience before changing numbers.

Source Traceability

Primary source: The Art of Game Design: A Book of Lenses, Third Edition by Jesse Schell, especially chapters 12-13 on chance, expected value, fairness, challenge, meaningful choices, rewards, punishment, and economies. The workflow is transformed and paraphrased.

Supporting sources include MDA for reasoning from mechanics to dynamics and accessibility guidance for difficulty options that preserve player agency.

Workflow

  1. Define the target experience: tense, generous, punishing, expressive, strategic, chaotic, readable, or mastery-driven.
  2. Map resources, sinks, sources, rewards, costs, probabilities, time, and failure penalties.
  3. Find dominant strategies, dead choices, runaway feedback, grind, scarcity collapse, and opaque randomness.
  4. Use expected value and simulation where useful, but validate with playtests.
  5. Produce a tuning table with ranges, defaults, rationale, and telemetry.

Required Output

  • Balance Intent: what the numbers should make players feel and do.
  • Economy Map: sources, sinks, currencies, rewards, gates, and conversion rates.
  • Tuning Table: parameters, default values, safe ranges, and intended effects.
  • Risk Review: dominant strategies, exploits, fairness issues, and accessibility concerns.
  • Validation Plan: spreadsheet checks, script checks, playtest tasks, and telemetry.

Helper Script

Use scripts/expected_value.py for quick expected value checks:

python3 path/to/game-balance-economy/scripts/expected_value.py --outcome 0:0.5 --outcome 10:0.5

Local References

Before producing a tuning plan, read:

  • references/core/guide.md
  • workflows/tuning-pass.md

Comments

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