Tune the exact WORDING of model-right-sizer's four already-shipped research-grounded layers to maximize real-execution accuracy (effort stayed within budget, per `classify_budget_adherence`) — not whether to include a layer (see `model-right-sizer-layer-ablation`), but how an included layer should be phrased. Runs a discrete coordinate-ascent search (the finite-difference analog of gradient descent for prose) over four wording knobs in `eval/tuning/knobs.py`, each anchored at one spot in the shipped agent text plausibly moving the accuracy ratio: `token_ceiling` margin, how hard the effort dial leans down under difficulty-uncertainty, and the calibration/adherence knobs. Read-mostly: never edits the agent file directly, only proposes the winning wording as a diff to review. Use when someone says "tune model-right-sizer's wording for accuracy", "optimize the budget-ceiling wording", "run a gradient descent / hill-climbing search on the agent prompt", or "which wording maximizes budget-adherence accuracy".