Evaluate full

Skill tchr-dev/autonovel/.claude/skills/evaluation/evaluate-full

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

Install

npx -y skills add tchr-dev/autonovel --skill evaluate-full

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

Holistic novel-level evaluation — arc completion, pacing curve, theme coherence, foreshadowing resolution, world/voice consistency, overall engagement. Returns the weakest chapter and the top single suggestion. Use after first draft and between revision cycles.

SKILL.md

2.1 KB, as published. Nobody here has run it

Evaluate full novel

You are a literary critic. JSON only.

Inputs (from `<novel-dir>/`)

voice.md, world.md (truncate ~3000 words), characters.md, outline.md (full, including foreshadowing ledger)
All chapters/ch_*.md files

Build chapter summaries

For each chapter, compose:

Chapter N (W words):
  Opening: <first 500 chars>...
  Closing: ...<last 500 chars>

Concatenate into the prompt context. (For very long manuscripts, you may also include each chapter's most recent eval JSON if one exists in eval_logs/.)

Score 0-10

arc_completion — do character arcs resolve satisfyingly?
pacing_curve — does tension build properly across the book? (This is usually the stubborn score; 7 is often a structural ceiling.)
theme_coherence — themes explored consistently?
foreshadowing_resolution — all planted threads harvested?
world_consistency — any lore contradictions across chapters?
voice_consistency — voice steady throughout? Use the scripts/voice_fingerprint.py outliers if available.
overall_engagement — compelling read start to finish?

Output JSON (to `<novel-dir>/eval_logs/<timestamp>_full.json`)

{
  "arc_completion": {"score": N, "note": "..."},
  "pacing_curve": {"score": N, "note": "..."},
  "theme_coherence": {"score": N, "note": "..."},
  "foreshadowing_resolution": {"score": N, "note": "..."},
  "world_consistency": {"score": N, "note": "..."},
  "voice_consistency": {"score": N, "note": "..."},
  "overall_engagement": {"score": N, "note": "..."},
  "novel_score": N,
  "weakest_dimension": "...",
  "weakest_chapter": N,
  "top_suggestion": "..."
}

After writing

Print the one-line summary. Note any chapter whose score has rotated as "weakest" — chasing a rotating weakest chapter is whack-a-mole; after 2 rotations, stop revising on that axis.