SkillsAdd

egorfedorov/slot-casino-game-developer-skills-for-stake-engine

rtp-optimizer

Optimize and validate slot/casino RTP against explicit targets using simulation evidence.

First seen Mar 10, 2026

Installation

$ npx skills add egorfedorov/slot-casino-game-developer-skills-for-stake-engine --skill rtp-optimizer
View on skills.sh ↗ GitHub repository ↗

Summary

  • Optimize and validate slot/casino RTP against explicit targets using simulation evidence.
  • Use when defining RTP targets per mode, tuning paytables and feature frequencies, validating convergence from simulation runs, comparing theoretical vs empirical RTP, or preparing release sign-off with pass/fail thresholds.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 53
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,560 B
  • docs SUMMARY.md 334 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 17 installs

SKILL.md

RTP Optimizer

Use this skill to move a game from rough math to quantifiably validated RTP.

Workflow

  1. Define targets and guardrails first.
  • Capture RTP target by mode, tolerance band, max win cap, volatility expectations, and feature frequency limits.
  • Mark any missing constraint as an explicit assumption.
  1. Identify controllable tuning levers.
  • Prioritize levers with predictable RTP effect: symbol payouts, reel strips, feature trigger weights, bonus multipliers, and retrigger caps.
  • Avoid changing multiple high-impact levers at once unless required.
  1. Run iterative simulation with convergence checks.
  • Use short runs for direction (>=1M spins), then long runs for sign-off (>=20M spins).
  • Track seeds, config hash/version, and lever deltas per run.
  • Reject sign-off if mean RTP is outside tolerance or confidence interval crosses tolerance boundaries.
  1. Cross-check theoretical and artifact-weighted RTP.
  • Compare model RTP, simulator RTP, and weighted book RTP.
  • Treat unresolved drift between these sources as a blocker.
  1. Prepare optimization sign-off.
  • Deliver run summary, lever changes, pass/fail verdict, and residual risks.
  • Include exact patch plan and verification commands.

Commands

python3 scripts/evaluate_rtp_runs.py \
  --input <runs.jsonl> \
  --target-rtp 0.9600 \
  --tolerance 0.0020

Use this command to produce deterministic convergence and pass/fail output for a run set.

Output Contract

Return:

  1. Targets: mode targets, tolerance bands, assumptions.
  2. Lever Plan: changed levers and expected RTP direction.
  3. Run Results: mean RTP, CI, drift, pass/fail verdict.
  4. Patch Plan: exact files/functions requiring edits.
  5. Residual Risks: blockers or statistical uncertainty.

References

  • references/workflow.md: tuning lifecycle and sequencing.
  • references/tuning-levers.md: common lever impact and failure patterns.
  • references/signoff-template.md: concise handoff template.

Execution Rules

  • Keep theoretical and simulated RTP separated in reporting.
  • Require reproducible run metadata (seed, spins, config version).
  • Treat tolerance breach or unstable convergence as release blockers.