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/recovered/embed.py

@@ -0,0 +1,34 @@
+"""Embed chunks on the local endpoint, checkpointing as we go."""
+import json, pathlib, ssl, time, urllib.request
+
+URL = 'https://inference.lan:8443/v1/embeddings'   # placeholder: the real host is never committed
+MODEL = 'nomic-embed-text'
+BATCH = 24
+ctx = ssl.create_default_context(); ctx.check_hostname = False; ctx.verify_mode = ssl.CERT_NONE
+
+chunks = json.loads(pathlib.Path('chunks.json').read_text(encoding='utf-8'))
+out = pathlib.Path('embeddings.json')
+done = json.loads(out.read_text()) if out.exists() else []
+
+start = len(done)
+print(f'{len(chunks)} chunks, resuming at {start}', flush=True)
+t0 = time.time()
+for i in range(start, len(chunks), BATCH):
+    batch = [c['text'] for c in chunks[i:i + BATCH]]
+    body = json.dumps({'model': MODEL, 'input': batch}).encode()
+    req = urllib.request.Request(URL, data=body, headers={'Content-Type': 'application/json'})
+    for attempt in range(4):
+        try:
+            with urllib.request.urlopen(req, timeout=180, context=ctx) as r:
+                d = json.load(r)
+            done.extend(e['embedding'] for e in d['data'])
+            break
+        except Exception as e:
+            if attempt == 3: raise
+            print(f'  retry {attempt+1} at {i}: {e}', flush=True)
+            time.sleep(3 * (attempt + 1))
+    out.write_text(json.dumps(done))
+    el = time.time() - t0
+    n = len(done)
+    print(f'  {n}/{len(chunks)}  {el:.0f}s  ({n/max(el,1):.1f}/s)', flush=True)
+print(f'done: {len(done)} embeddings in {time.time()-t0:.0f}s')