T4 (MarginFactor): scored outcomes via Gaussian-margin EP evidence

Adds soft Gaussian-observation evidence on the per-pair diff variable,
enabling continuous score margins as a richer alternative to ranks.

Public API:
- `Outcome::Scored([scores])` (non-breaking enum extension under
  `#[non_exhaustive]`).
- `Game::scored(teams, outcome, options)` constructor parallel to
  `Game::ranked`.
- `EventBuilder::scores([...])` fluent helper.
- `HistoryBuilder::score_sigma(σ)` knob (default 1.0, validated > 0).
- `GameOptions::score_sigma`.
- `EventKind` re-exported from `lib.rs` (annotated `#[non_exhaustive]`).
- New `InferenceError::InvalidParameter { name, value }` variant.

Internals:
- `MarginFactor` (`factor/margin.rs`): Gaussian observation factor that
  closes in one EP step; cavity-cached log-evidence mirrors `TruncFactor`.
- `BuiltinFactor::Margin` dispatch arm.
- `DiffFactor` enum in `game.rs` lets `Game::likelihoods` and the new
  `likelihoods_scored` share the per-pair link abstraction.
- Per-event `EventKind { Ranked, Scored { score_sigma } }` routed through
  `TimeSlice::add_events`, `iteration_direct`, and `log_evidence`.

Tests: 88 lib + 27 integration (4 new in `tests/scored.rs`); existing
goldens byte-identical.  Bench: `benches/scored.rs` baseline ~960µs for
60 events × 20-player pool with default convergence.

Plan: docs/superpowers/plans/2026-04-27-t4-margin-factor.md
Spec item marked Done.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tests/api_shape.rs

@@ -223,3 +223,26 @@ fn predict_outcome_two_teams_sums_to_one() {
     assert!((p[0] + p[1] - 1.0).abs() < 1e-9);
     assert!(p[0] > p[1]);
 }
+
+#[test]
+fn fluent_event_builder_scores() {
+    use trueskill_tt::ConstantDrift;
+    let mut h = History::builder()
+        .mu(25.0)
+        .sigma(25.0 / 3.0)
+        .beta(25.0 / 6.0)
+        .drift(ConstantDrift(0.0))
+        .build();
+
+    h.event(1)
+        .team(["alice"])
+        .team(["bob"])
+        .scores([12.0, 4.0])
+        .commit()
+        .unwrap();
+    h.converge().unwrap();
+
+    let a = h.current_skill(&"alice").unwrap();
+    let b = h.current_skill(&"bob").unwrap();
+    assert!(a.mu() > b.mu());
+}