phase5: learned class-selection policy (SPEC §13) — rank classes by ledger evidence

crates/policy: reads the append-only ledger and ranks playbook classes by
the Wilson score lower bound of their machine-verified win rate — the same
'quote the lower bound' discipline the rest of the project uses. A class
that won 1/1 does not outrank one that won 8/10 (wider interval); untried
classes fall back to the cheapest-first prior; tried-and-failed classes
rank last. Advisory only — the gates still decide every verdict.

On the current 33-row ledger it ranks class 5 (algorithmic) first, then
class 3, then class 1 — the proven winners — with class 7 (untried) above
the evidence-against classes. Wins count only machine-sanitizer-verified
accepts (conservatively excluding pre-gate accepts and the overturned
comrak-010): the policy trusts the ledger's machine record, not narrative.
4 unit tests (Wilson monotonicity, small-sample penalty, ranking order).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2

crates/policy/Cargo.toml

@@ -0,0 +1,12 @@
+[package]
+name = "policy"
+description = "Learned class-selection policy (SPEC §13): rank playbook classes by the Wilson lower bound of their observed win-rate in the ledger, degrading to cheapest-first when data is thin"
+version.workspace = true
+edition.workspace = true
+license.workspace = true
+repository.workspace = true
+
+[dependencies]
+anyhow.workspace = true
+clap.workspace = true
+ledger = { path = "../ledger" }