Add story-to-pack research and a structured situation review

Research toward building a content pack from a story corpus, kept on its own
branch and independent of the game. Records the selection experiments against
blind labels, and settles selection as gate G2 followed by a human review:
review.py writes REVIEW.md and a review.json form, apply_review.py checks the
filled form and writes situations.json for the next stage.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01C6UDQ9o6L6Ey173U7XVou6

tools/story-to-pack/probe/embed.py

@@ -0,0 +1,37 @@
+"""Embed texts, checkpointing after every batch so an interrupted run resumes.
+
+  python3 embed.py chunks.json embeddings.json               # the scenes' prose
+  python3 embed.py summaries.json summary_embeddings.json    # one-line situations
+  python3 embed.py chunks.json embeddings.json --limit 48    # pilot
+
+Input is a JSON list of objects with a 'text' field (chunks.json), or of
+strings/objects with a 'summary' field (summaries.json).
+"""
+import argparse, json, os, pathlib, time
+import ollama
+
+ap = argparse.ArgumentParser()
+ap.add_argument('src'); ap.add_argument('dst')
+ap.add_argument('--limit', type=int, default=None)
+ap.add_argument('--batch', type=int, default=16)
+args = ap.parse_args()
+MODEL = os.environ.get('STP_EMBED_MODEL', 'nomic-embed-text')
+
+items = json.loads(pathlib.Path(args.src).read_text(encoding='utf-8'))
+if isinstance(items, dict): items = items['summaries']
+texts = [x if isinstance(x, str) else (x.get('summary') or x['text']) for x in items]
+if args.limit: texts = texts[:args.limit]
+
+out = pathlib.Path(args.dst)
+state = json.loads(out.read_text()) if out.exists() else {'model': MODEL, 'vectors': []}
+if state['model'] != MODEL: raise SystemExit(f'{out} was made with {state["model"]}, not {MODEL}')
+done = state['vectors']
+print(f'{len(texts)} texts, resuming at {len(done)}, model {MODEL}', flush=True)
+
+t0, start = time.time(), len(done)
+for i in range(start, len(texts), args.batch):
+    done.extend(ollama.embed(MODEL, texts[i:i + args.batch]))
+    out.write_text(json.dumps(state))
+    el = time.time() - t0
+    print(f'  {len(done)}/{len(texts)}  {el:.0f}s  ({(len(done) - start) / max(el, 1):.2f}/s)', flush=True)
+print(f'done: {len(done)} vectors, {time.time() - t0:.0f}s this session')