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
2026-09-15 06:59:35 -04:00
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+"""Dump candidate clusters to blind label sheets (SELECTION.md, protocol steps 1-3).
+
+  python3 make_sheets.py
+
+Writes, for development (seed 11) and test (seed 12):
+  candidates-<set>.json   cluster id -> member chunk indices (what signals are computed on)
+  sheet-<set>.txt         per cluster, member summaries only, shuffled; no titles, no scores
+  labels-<set>.json       an empty template to fill in: {id: {"label": null, "name": ""}}
+
+Cluster ids are assigned in a shuffled order so position carries no information.
+"""
+import json, pathlib, random, sys
+
+K = 60
+SETS = {'dev': 11, 'test': 12}
+
+sys.argv = ['measure.py', 'summary_embeddings.json']
+exec(pathlib.Path('measure.py').read_text(encoding='utf-8').split("print(f'{path.name}")[0])
+
+summaries = json.loads(pathlib.Path('summaries_clean.json').read_text(encoding='utf-8'))['summaries']
+text = {x['chunk']: x['summary'] for x in summaries}
+
+for name, seed in SETS.items():
+    cands = []
+    for c in kmeans(K, seed):
+        size, nst, dom, _ = describe(c)
+        if size >= MIN_SIZE and nst >= MIN_STORIES and dom <= MAX_DOMINANT:
+            cands.append(sorted(c))
+    rnd = random.Random(seed * 1000 + 7)
+    rnd.shuffle(cands)
+    prefix = name[0].upper()
+    ids = {f'{prefix}{i + 1:02d}': c for i, c in enumerate(cands)}
+
+    pathlib.Path(f'candidates-{name}.json').write_text(json.dumps(ids, indent=1))
+    lines = []
+    for cid, members in ids.items():
+        shown = members[:]
+        rnd.shuffle(shown)
+        lines.append(f'=== {cid}')
+        lines.extend(f'  - {text[i]}' for i in shown)
+        lines.append('')
+    pathlib.Path(f'sheet-{name}.txt').write_text('\n'.join(lines), encoding='utf-8')
+    labels = pathlib.Path(f'labels-{name}.json')
+    if not labels.exists():
+        labels.write_text(json.dumps({cid: {'label': None, 'name': ''} for cid in ids}, indent=1))
+    print(f'{name}: seed {seed}, {len(ids)} candidate clusters, {sum(map(len, ids.values()))} scenes')