todo(12): open

TODO

@@ -191,3 +191,26 @@ Description: operate_rate computes sum(t.steps > 0 and t.meta.get('clean') for t
              and resident.py:157. Wrap the predicate in bool() so it counts
              trajectories that operated and are clean, fixing both the type
              error and the latent crash.
+
+--ISSUE
+Content-Type: application/issue
+ID: 12
+Type: feature
+Title: load training data from a raw dir, a curated jsonl, or the Hub
+Status: open
+Priority: medium
+Created: 2026-06-17
+Module: sekft
+Relationships: 
+Description: iter_keepers reads only raw per-trajectory .json - one of three
+             input shapes the trainer should accept. Add load_turns(data, hub,
+             revision) that yields assistant-bearing turns from: a directory of
+             raw rollout .json (keep-filtered, today's iter_keepers); a curated
+             .jsonl corpus file (already keep-filtered, yield turns per line);
+             or the published corpus via posix-sdc's load_trajectories (local
+             data/ in a checkout, else the Hub). sekft-train gains --hub and
+             --revision; --data dispatches by dir-vs-.jsonl. Raw-rollout reading
+             stays sekft-local; curated+Hub reuse posix-sdc's loader (imported
+             lazily so the trainer needs neither posix-sdc nor huggingface_hub
+             for the raw/jsonl paths). Unit tests for the raw-dir and jsonl
+             dispatch.