Turn the data we already have into an open, educational dataset, so the trace we
are about to start collecting has somewhere to go.
`migrate.py` produces:
* runs.jsonl 37 runs with outcome, killer, seed and final deck
* decisions.jsonl 1052 decisions, each with its own outcome attached
* states_index.jsonl 346 unique observations
* states/ content-addressed gzipped blobs
Content addressing matters: measured, only 56% of captures are unique, so 44% of
storage is duplicates. 3.28 MB raw -> 0.42 MB stored.
The card-reward rows keep the REJECTED options, so this is a ranking dataset
rather than a classification one, and the per-fight `damage_taken` /
`turns_taken` pair is the dense reward signal a combat policy is judged on.
What it deliberately does NOT do: reconstruct per-step combat state/action pairs.
The session logs record the action but not the observation, and captures exist
only for combat, so a step has a state with no action or an action with no state
-- never both. Inventing them would poison the corpus. The gap is declared in
manifest.json instead, and collect.py will close it going forward.
Integrity checking is a separate entry point (`--check-only`) because `--verify`
alone rebuilds first and so can only ever see data that is correct by
construction -- a smoke test pretending to be a check. All six invariants were
verified by deliberately breaking the dataset and confirming the checker fails.
