How do I install the Rtp Optimizer skill?

Run npx skillmds@latest add egorfedorov/rtp-optimizer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Rtp Optimizer safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Rtp Optimizer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Rtp Optimizer free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Rtp Optimizer?

egorfedorov (@egorfedorov) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Rtp Optimizer by egorfedorov — Agent Skill

Name: Rtp Optimizer
Author: 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.

egorfedorov 6ef69a3 6 files · 9.8 KB Updated Sep 17, 2026

File contents

RTP Optimizer

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

Workflow

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.

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.

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.

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.

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:

Targets: mode targets, tolerance bands, assumptions.
Lever Plan: changed levers and expected RTP direction.
Run Results: mean RTP, CI, drift, pass/fail verdict.
Patch Plan: exact files/functions requiring edits.
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.

---name: rtp-optimizerdescription: 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.---# RTP OptimizerUse this skill to move a game from rough math to quantifiably validated RTP.## Workflow1. 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.2. 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.3. 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.4. 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.5. Prepare optimization sign-off.- Deliver run summary, lever changes, pass/fail verdict, and residual risks.- Include exact patch plan and verification commands.## Commands```bashpython3 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 ContractReturn: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.

Frequently asked questions