chapter-summarise

name	chapter-summarise
description	Generate structured session-summary.md files from campaign chapter PROSE, for chapters that have no session recording behind them — the case where the extraction ladder would otherwise be stuck on raw prose and the event spine has no scene key. Opus orchestrates, one Haiku subagent per chapter, writing to summaries/haiku/<NNN>-<slug>/. Generation is constrained (no invented dialogue, no module-canon backfill, no silent typo fixes, must compress) and every output is then checked by a deterministic verifier that the GM reviews — the model's self-report is never the gate. Surfaces chapter typos and name drift as a fix-at-source queue rather than papering over them. Invoke as /chapter-summarise [campaign-dir] [chapter-range].
tools	Read, Bash, Write, Edit, Glob, Grep, Agent, AskUserQuestion

chapter-summarise

Build session-summary.md files from chapter prose so that chapters with no transcript can still feed the summary rung of the extraction ladder and give the event spine a real scene key.

Read this first: when NOT to run

If a chapter has a session workspace with a VTT, do not generate a summary for it. That workspace's session-summary.md came from enhance_summary reading the actual transcript; it sits upstream of the chapter prose. A generated summary sits downstream — it can only ever lose or invent information relative to its source. Generating one would be LLM extracts → LLM structures → LLM renders, the compounding pattern the project rule prohibits.

Check before you start:

cd <campaign-dir>
find summaries -name '*.vtt' | sed 's|/[^/]*$||' | sort -u

Any chapter covered by one of those directories is out of scope. This skill is only for the chapters left over — typically the early campaign, before recording started.

Be honest with the GM about what this buys: the chapter prose remains the most upstream artifact available. A generated summary does not add information. What it adds is structure — explicit scene boundaries, which the prose lacks and which the spine needs.

What the structure is worth

event_spine rows key on (chapter, scene, seq). scene is scene_index, stamped by ensemble_merge.stamp_scene_index from campaignlib.textproc. chunk_by_scenes — header-driven. So the source document's headings literally become the scene key.

Early-campaign chapters are usually organised by in-world date (## 8/1 of Taraksh 1495) with POV names beneath (### Soma), or have no ## heading at all. Either way scene ends up meaning "which day" or "which POV block", or nothing. A generated summary's ### <Scene title> list gives real scenes.

The catch, and it is easy to miss: chunk_by_scenes splits on ## and only consults ### when there are no ## headings anywhere. The summary's own ## Summary / ## Scenes / ## NPCs are H2s, so feeding the whole file yields three or four chunks and collapses every scene into one. Slicing to the ## Scenes section alone does not help either — ## Scenes is still an H2.

Per-scene chunking requires the section body with the ## Scenes wrapper line stripped, leaving ### titles as the only headings:

sec  = re.search(r'(?ms)^##\s+Scenes\b.*?(?=^##\s+(?!#)|\Z)', text).group(0)
body = re.sub(r'(?m)^##\s+Scenes\b.*\n', '', sec)     # -> convention='h3'

And note the tension: campaignlib.lineage._summary_is_structured requires a literal ## Scenes and ## NPCs to admit the file to the summary rung at all. The ladder gate demands the wrapper; the chunker is defeated by it. One document cannot satisfy both — this needs a slicing pre-pass at extraction time, not a format change. Tell the GM this rather than quietly picking one.

Procedure

1. Scope and confirm

Resolve the campaign dir and the chapter range. Establish which chapters have no VTT (above). Report the count and the total word volume before spending tokens.

Check for duplicate chapter indices — a re-split after a spelling fix leaves orphans behind:

python3 -c "
import re,glob
from collections import Counter
c=Counter(int(re.match(r'.*chapter_(\d+)_',f).group(1)) for f in glob.glob('docs/chapters/chapter_*.md'))
print([k for k,v in c.items() if v>1] or 'no duplicates')"

If there are duplicates, resolve them with the GM before generating — pick the newest file per index, and list the strays for the GM to delete. Do not delete chapter files yourself. Confirm which spelling is canonical by counting both against the bible, not by guessing.

2. Build the plan

One JSON entry per chapter: ch, chapter (input path), out (output path), words. Output layout, zero-padded so lexical sort matches chapter order:

summaries/haiku/<NNN>-<chapter-slug>/session-summary.md
   e.g. summaries/haiku/002-arrival_in_phandalin/session-summary.md

Reuse the chapter filename's own slug. Create the directories up front.

3. Generate — one Haiku subagent per chapter

Copy summary_spec.md (bundled with this skill) somewhere the subagents can read, or point them at it in place. Launch subagents in batches of about 10, in a single message each so they run concurrently. Each prompt gives:

• the campaign root, the spec path, and the plan path
• its chapter number, and an instruction to take input/output paths from the plan
• a warning that a deterministic verifier will check quotes, proper nouns and word count, and that self-reports are not accepted
• for short chapters (< ~900 words), an explicit "do not pad" note
• for chapters where a previous pass is known to have fabricated, a specific heads-up about what it invented

Use model: haiku. Ask each agent to reply with only three numbers: words written, chapter words, quoted strings. These are for triage, not truth.

4. Verify — this is the gate

python3 ~/.claude/skills/chapter-summarise/verify_summaries.py \
    --campaign-dir <campaign-dir> --summaries-dir summaries/haiku

Exit 2 on a setup problem (no summaries, or two directories claiming the same chapter index — it refuses rather than verifying the wrong file). Exit 1 on a content failure: missing file, gate fail, ratio >= 1.0, or a flagged quote.

Regenerate on gate/ratio failures — those are unambiguous. A flagged quote is not automatically a regenerate; adjudicate it first (see below), because an innocent flag will otherwise loop forever.

Read the output honestly rather than reporting the headline:

• order = unverif means no scene in that chapter could be anchored to a traceable string. That is unverified ordering, not verified — say so. A chapter whose bullets are all heavy paraphrase will read as unverif.
• Flagged quotes are candidates, not verdicts. Check each by hand. In the reference run both flags were innocent: one stitched two real fragments into one quote, and one silently corrected a typo in the chapter (lightbrining → lightbringing). The second is a finding about the source, not the summary.
• Novel proper nouns leak ordinary words (sentence-initial Throughout, Compelled). Skim for real names. Genuine hits are usually module-canon bleed — a name from the published adventure that is not in the chapter.

5. Hand back a fix-at-source queue

Do not suppress what the verifier finds. Chapter typos, name drift, and lore bleed should be reported to the GM as a list to fix in the bible and the chapters, because that is where the next re-split will read from. Patching a generated summary fixes nothing; the defect regenerates.

Check the bible directly:

grep -oci 'Axelholm\|Axeholm' docs/<bible>.md

Reference run (Phandalin, chapters 2-30, 2026-08-02)

29 chapters, 55k words of prose, one Haiku agent each, three batches.

check	this skill	prior unconstrained pass
gate pass	29/29	29/29
no ## Memorable Moments	29/29	0/29
shorter than source	29/29	11/28
dialogue quotes	73	87
— with no trace in chapter	2 (both innocent)	14
novel proper nouns	18 (mostly artifacts)	434
scenes produced	148, all h3-chunking cleanly	164

The prior pass's failure modes, all of which the constraints above target: invented attributed dialogue; expansion to as much as 2.9x the source; module-canon backfill (Toblen, Harbin, Menzoberranzan in chapters that never mention them); and silently changed roles and pronouns (a "wererat miner" with his became "the wererat leader" with She).

Known residual weaknesses:

• A ## NPCs section will balloon a short chapter past 100% if it writes an entry per proper noun — rule 9 in the spec exists for this, and chapter 1 of the pilot still failed it at 1.50x before the rule was added.
• The verifier catches invented names and invented quotes. It does not catch invented claims about real entities. A paragraph elaborating on a real NPC from model knowledge passes every check. Chapters that are mostly ## NPCs prose deserve a human read.
• Compression near 0.95 (chapter 14 hit 0.94) passes the rule but is not really a summary. Consider flagging anything above ~0.85 for the GM.