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/godot-monte-carlo-balancer

@9d6e91e
by Divergent AIthedivergentai/gd-agentic-skills783 stars
48

Use when auditing or recalibrating game balance: build a source-driven Monte Carlo balance lab (Rust + rayon) that extracts live game data, simulates human playstyles (AFK→pro), emits win-rate/economy verdicts with confidence intervals, and bruteforce-tunes parameters. Trigger on unfair levels, unreachable shops, farm exploits, interest-curve cliffs, post-content recalibration, or CI balance JSON diffs. Keywords: balance lab, Monte Carlo, win rate, difficulty curve, economy career, playstyle simulation, Resource extraction, GDScript parser, bruteforce tuning.

Ask your Agent

Use this Skill: https://skilld.dev/gh/thedivergentai/gd-agentic-skills/godot-monte-carlo-balancer

This session only. Nothing lands on disk.

referencesjson-schema.md

≈597 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Balance Lab JSON field contract

Stable names for --json output. Agents and CI must not rename these without a version bump (schema_version).

Top-level envelope

{
  "schema_version": 1,
  "command": "simulate",
  "seed": 42,
  "runs": 1000,
  "game_data_hash": "0164hex-or-u64-string",
  "cells": [ /* Aggregate */ ]
}

Aggregate (matrix cell)

Field Type Notes
session_id int/string Level / floor / shift id
style string afk, casual, …
loadout_id string Stable id
n int Runs in cell
win_rate float Point estimate 0–1
win_rate_ci_low float Wilson/bootstrap low
win_rate_ci_high float Wilson/bootstrap high
verdict string OK / TOO_HARD / TOO_EASY / INCONCLUSIVE
grade_hist object e.g. {"0":10,"1":20,"2":30,"3":40}
time_avg float Seconds
currency_per_minute float optional; modes/careers
warnings string[] anomaly callouts

Game-specific texture metrics may appear under metrics (object) without breaking CI that only diffs envelope + core fields.

inspect envelope

{
  "schema_version": 1,
  "command": "inspect",
  "game_data_hash": "...",
  "defaults_used": [ {"path": "enemies[2].hp", "value": 10} ],
  "catalogs": { }
}

Career envelope

Include purchases (array of {item_id, minute_median, minute_p90}), replay_vs_frontier_ratio, and flags (UNREACHABLE_SHOP, DOMINANT_FARM, …).

Diff rules

compare_balance_snapshots.py keys cells by (session_id, style, loadout_id). Refuse compare if schema_version, seed, or runs disagree (unless --force).

<!-- GDSkills research links (agents) Official docs: - https://docs.godotengine.org/en/stable/tutorials/scripting/resources.html - https://docs.godotengine.org/en/stable/classes/class_json.html - https://docs.godotengine.org/en/stable/tutorials/editor/command_line_tutorial.html Related skills: - https://github.com/thedivergentai/gd-agentic-skills/blob/main/skills/godot-resource-data-patterns/SKILL.md — Resource-first extract - https://github.com/thedivergentai/gd-agentic-skills/blob/main/skills/godot-testing-patterns/SKILL.md — Phase 7 headless calibration Parent skill: https://github.com/thedivergentai/gd-agentic-skills/blob/main/skills/godot-monte-carlo-balancer/SKILL.md -->

Source: SKILL.md on GitHub

Third-party checks

No alerts1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    This skill provides a robust framework and Rust-based templates for creating Monte Carlo game balance simulations in Godot 4.7. It includes automated build scripts, source-driven data extraction patterns, and statistical analysis tools. Security analysis confirms the skill follows safe practices, utilizing trusted external resources and standard development workflows.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW · No issues

Provenance

Signed by skilld at 9d6e91e. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 weeks ago.

Activeupdated 2 months ago

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