docs(skills): tighten 'rubric scale.' spacing in eval-campaign gate note (#174)

Co-authored-by: Drew Stone <drew@tangle.tools>

Author: drewstone

.claude/skills/eval-campaign/SKILL.md

@@ -89,7 +89,7 @@ if (result.gate.decision === 'ship') await ship(result.winnerSurface)
 | `baselineSurface` | — (required) | the surface the loop optimizes; start `''` |
 | `mutationPrimitives` | gepaProposer's own | your optimization levers (additive directives) |
 | `proposer` | `gepaProposer` | pass `evolutionaryProposer({ mutator })` for blind addendum rotation |
-| `gate` | `defaultProductionGate` (Δ 0.05) | `paretoSignificanceGate` for multi-objective; tune `deltaThreshold` for your rubric scale . To prove a held-out lift is real CONTENT and not just added prompt/mount FOOTPRINT, compose `neutralizationGate` (agent-eval >= 0.107.0, from `@tangle-network/agent-eval/campaign`) — a footprint-matched placebo arm. It needs `ctx.neutralizedJudgeScores` from `runImprovementLoop({ neutralize })`; `selfImprove`/this scaffold do not surface that option yet, so wire it at the `runImprovementLoop` level or re-export it in the scaffold first. |
+| `gate` | `defaultProductionGate` (Δ 0.05) | `paretoSignificanceGate` for multi-objective; tune `deltaThreshold` for your rubric scale. To prove a held-out lift is real CONTENT and not just added prompt/mount FOOTPRINT, compose `neutralizationGate` (agent-eval >= 0.107.0, from `@tangle-network/agent-eval/campaign`) — a footprint-matched placebo arm. It needs `ctx.neutralizedJudgeScores` from `runImprovementLoop({ neutralize })`; `selfImprove`/this scaffold do not surface that option yet, so wire it at the `runImprovementLoop` level or re-export it in the scaffold first. |
 | `budget` | 3 gens × pop 2, 0.25 holdout | `budget.reps` (replicates → tighter CIs), `budget.promoteTopK`, `budget.holdoutScenarios` (explicit split), `budget.dollars` (cost cap) |
 | `expectUsage` | **`'assert'`** | the fail-loud backend-integrity guard. Leave at `'assert'` for real runs (a stub cell throws); set `'off'` ONLY for a deterministic offline/replay run |
 | `labeledStore` | off | capture every artifact + judge score (the dataset you ship + few-shot corpus); set `captureSource` (default `'eval-run'`) |