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Apply cuts

Skill tchr-dev/autonovel/.claude/skills/revision/apply-cuts

Autonomous fantasy-novel pipeline as Claude Code skills, agents, and slash commands

Install
npx -y skills add tchr-dev/autonovel --skill apply-cuts

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Apply adversarial-edit cuts to chapter files (mechanical quote-matching removal). Wraps scripts/apply_cuts.py. Filters by cut type (OVER-EXPLAIN, REDUNDANT typically yield ~55-60% of cuts). Use after adversarial-edit during revision cycles.

SKILL.md

1.8 KB, as published. Nobody here has run it

Apply cuts

Mechanical pass — no LLM call. Reads <novel-dir>/edit_logs/chNN_cuts.json produced by adversarial-edit and removes matching quotes from chapters.

Common invocations

# Dry run on all chapters with cuts files
AUTONOVEL_NOVEL_DIR=<novel-dir> python scripts/apply_cuts.py all --dry-run

# Apply only OVER-EXPLAIN and REDUNDANT cuts (the safest/most common types)
AUTONOVEL_NOVEL_DIR=<novel-dir> python scripts/apply_cuts.py all --types OVER-EXPLAIN REDUNDANT

# Apply all cuts to chapters where adversarial-edit found ≥17% fat
AUTONOVEL_NOVEL_DIR=<novel-dir> python scripts/apply_cuts.py all --min-fat 17

# Single chapter
AUTONOVEL_NOVEL_DIR=<novel-dir> python scripts/apply_cuts.py 12

Failure modes

  • not found — the quote doesn't appear in the chapter (often because a previous cut already shifted whitespace; the script tries whitespace-normalised match before giving up)
  • ambiguous (N matches) — the quote appears more than once; the script refuses to guess which instance was meant
  • Skipped if quote < 25 chars

The script collapses runs of 3+ newlines down to 2 after applying cuts.

After running

Report:

  • Total words removed across chapters
  • Per-chapter applied / failed / skipped breakdown
  • Which chapters had the highest cut counts (revision priorities)

Suggest:

  • Re-evaluate any heavily-cut chapter with evaluate-chapter to confirm the score moved in the right direction
  • If failed count is high, manually inspect the cuts file — some quotes may need rephrasing in the cuts JSON before retrying

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.