The Tuning Stack proves that progression ideas can be exercised through scenario breadth, pass bars, sensitivity notices, regression examples, and synthesis reports without canonizing game economy.
Tuning Lab
Tuning Stack Case Study
A benchmark-only lab for evaluating progression and economy behavior through deterministic artifacts before any real-game tuning decisions are allowed.
Strongest Evidence
- Artifact ownershipThe full local benchmark suite has an explicit manifest and named consumer modules.
- Scenario breadthBenchmarks cover core idle loop, roster progression, stochastic collection economy, insertion stress, tower-defense hybrid, and prestige pressure.
- Pass bars and warningsThe preview exposes pass bars plus a pressure-exponent sensitivity notice instead of hiding risk.
- Regression disciplineA failing regression example is preserved as evidence, not smoothed away.
Boundary: benchmark-only evidence is not real-game economy, balance, progression, or monetization canon.