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
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+# story-to-pack — recovered probe scripts
+
+Recovered 2026-09-11 from the Claude Code transcript of session
+`d1409289-cea9-4399-a827-2f13479647bc` (2026-09-10). The originals lived in that
+session's `/tmp` scratchpad and were lost when the machine rebooted. These are
+the last version of each file as the session wrote it. None of them has been
+re-run since recovery.
+
+This is research for a tool that builds a The Ladder content pack from a corpus
+of stories. It is deliberately independent of `theladder/` — it would live in
+that repo's `tools/`, never imported by `src/`, `content/` or `test/`, or on its
+own.
+
+| File | What it did |
+| --- | --- |
+| `probe.mjs`, `probe2.mjs`, `probe3.mjs` | Randomised the Frontier's numbers and ran the conformance checks: random magnitudes pass 3%, random directions 0%, a 4-rule repair loop 42% |
+| `split.py` | Splits Gutenberg O. Henry volumes into individual stories |
+| `chunk.py` | Cuts stories into ~300-word scenes on paragraph boundaries |
+| `embed.py` | Embeds scenes with `nomic-embed-text` on the LAN Ollama host |
+| `cluster.py` | k-means over the embeddings; reports how many stories each cluster draws from |
+| `sweep.py` | Sweeps k and measures coherence against cross-story spread |
+
+The probes expect to run from inside `theladder/` (they import its engine and
+packs). The Python scripts expect the corpus in a `corpus/` directory beside
+them.
+
+## Rebuilding the corpus
+
+O. Henry's *Four Million* cycle, Project Gutenberg ids 2776, 1444, 3707, 2141:
+
+```sh
+mkdir -p corpus
+for id in 2776 1444 3707 2141; do
+  curl -sL -o corpus/pg$id.txt "https://www.gutenberg.org/cache/epub/$id/pg$id.txt"
+done
+```
+
+That gave 94 stories, 244,690 words, 838 scenes. Embedding all of them took
+about 20 minutes (0.6 chunks/sec).
+
+## Where the research stopped
+
+- Clustering the raw prose failed. Only 3 of 18 candidate clusters recurred
+  across stories (2% of the corpus). At ~300 words, embedding similarity tracks
+  which story a scene is from, not what kind of situation it is.
+- The proposed fix, untested: summarise each scene into one generic sentence
+  with no proper names, embed the summary, then cluster.
+- It could not be tested because generation on the LAN Ollama host hung. The
+  model ran on CPU with no GPU offload, and the 16k-context variant stayed
+  loaded. That is probably the same fault that blocks Interactive Story M01.
+- The last open question was whether to use Claude for the generation steps
+  until that host is fixed. Embeddings stay on Ollama, because Anthropic has no
+  embeddings API.