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Rtp Optimizer

Community
egorfedorov
rtp-optimizer

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.

Overview

Publisheregorfedorov
RepositorySlot-Casino-Game-Developer-Skills-for-Stake-Engine
Skill namertp-optimizer
Stars
63
Forks
16
Bundled files
5
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by egorfedorov on GitHub. Read the source before you install it.

Installation

Install the Rtp Optimizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/egorfedorov/Slot-Casino-Game-Developer-Skills-for-Stake-Engine.git /tmp/Slot-Casino-Game-Developer-Skills-for-Stake-Engine
mkdir -p .claude/skills
cp -r /tmp/Slot-Casino-Game-Developer-Skills-for-Stake-Engine/rtp-optimizer .claude/skills/rtp-optimizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rtp Optimizer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Rtp Optimizer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Rtp Optimizer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Rtp Optimizer AI skill do?

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.

Why use Rtp Optimizer on TypingMind?

Because you install it once and use it with any model. Rtp Optimizer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Rtp Optimizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/egorfedorov/Slot-Casino-Game-Developer-Skills-for-Stake-Engine/tree/main/rtp-optimizer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Rtp Optimizer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Rtp Optimizer?

As many as you like. As long as a model supports skills, you can use Rtp Optimizer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Rtp Optimizer AI skill free?

It is published on GitHub by egorfedorov. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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