feat: add learned partial mirror planner

[bherald]

Summary

• add a learned partial-mirror planner driven by .coli_usage
• inspect safetensor headers directly and retain gate-projection dependencies for selected expert shards
• enforce copy budget and free-space reserve, reject unsafe/duplicate paths and malformed metadata, and fail closed without usage evidence
• stage copies atomically, fsync, verify SHA-256, and publish a readiness receipt without moving or deleting authoritative shards

Why

Very large disk-streamed MoE models may span multiple volumes. Mirroring the empirically hottest complete shard/dependency set onto a faster or less contended volume can be tested safely without changing the authoritative model layout.

Validation

• complete native suite plus 150 Python tests passed, 10 skipped, after rebasing onto JustVugg/colibri:dev revision cd278c227cf515b1f123c5fcc6bcf3c4820eafc9
• focused planner tests cover learned ranking, direct safetensor metadata, dependency inclusion, missing-usage refusal, traversal rejection, copy, receipt readiness, and full hash verification
• read-only real-model plan observed 1,369,800 selections and admitted 71 shards / 188,562,803,696 bytes under a 200-GiB budget while preserving a 100-GiB reserve

No model files were copied for the real-model validation; planning alone never activates a mirror.

[JustVugg]

Thanks — a learned mirror planner for dual-SSD is a sensible tool, and it's cleanly isolated (Python mirror_plan.py + launcher + test, no engine core touched), so it's low-risk. But it's +636 lines of a feature that isn't on the current critical path (the focus is getting the large models running on a single box), and I haven't given it the deep review that size deserves. So I'm parking it as not-priority-now rather than merging on a surface pass — it stays open and mergeable. When the dual-SSD path (#421) is something we're actively tuning, this is the natural companion. No rush on your side.