#autonovel

An autonomous pipeline for writing, revising, typesetting, illustrating, and narrating a complete novel. From a seed concept to a print-ready PDF, ePub, audiobook, and landing page — all generated by AI agents.

Inspired by karpathy/autoresearch: the same modify-evaluate-keep/discard loop, applied to fiction.

First novel produced: The Second Son of the House of Bells — 19 chapters, 79,456 words. See the autonovel/bells branch.

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#Quick Start

# Clone and setup
git clone <repo-url> && cd autonovel
cp .env.example .env    # Add your API keys

# Install dependencies
uv sync

# Generate a seed concept (or write your own in seed.txt)
uv run python seed.py

# Run the full pipeline
uv run python run_pipeline.py --from-scratch

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#The Pipeline

#Phase 1: Foundation

Build the world, characters, outline, voice, and canon from a seed concept. Loop until foundation_score > 7.5.

#Phase 2: First Draft

Write chapters sequentially. Evaluate each one. Keep if score > 6.0, retry if not. Forward progress over perfection.

#Phase 3a: Automated Revision

Adversarial editing → apply cuts → reader panel → generate briefs → rewrite chapters. Plateau detection stops the loop when scores stabilize.

#Phase 3b: Opus Review Loop

Send the full manuscript to Claude Opus for dual-persona review (literary critic + professor of fiction). Parse actionable items. Fix the top issues. Repeat until the reviewer runs out of major items.

#Phase 4: Export

Rebuild docs, typeset in LaTeX, generate art, produce audiobook scripts, build ePub, create landing page.

See PIPELINE.md for the full technical specification.

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#Tools (27 Python scripts)

#Foundation

#Drafting

#Evaluation

#Revision

#Art & Cover

#Audiobook

#Orchestration

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#File Structure

FRAMEWORK (reusable, on master):
  program.md             — Agent instructions per phase
  CRAFT.md               — Craft education (plot, character, world, prose)
  ANTI-SLOP.md           — Word-level AI tell detection
  ANTI-PATTERNS.md       — Structural AI pattern detection
  PIPELINE.md            — Full automation specification
  WORKFLOW.md            — Step-by-step human guide

TEMPLATES (filled per-novel on a branch):
  voice.md               — Part 1: guardrails. Part 2: discovered per novel
  world.md               — World bible template
  characters.md          — Character registry template
  outline.md             — Chapter outline template
  canon.md               — Hard facts database
  MYSTERY.md             — Central mystery (author-only)
  state.json             — Pipeline state tracker

TYPESETTING:
  typeset/novel.tex      — LaTeX template (EB Garamond, trade paperback)
  typeset/build_tex.py   — Chapters → LaTeX with vector ornaments
  typeset/epub_*          — ePub metadata, CSS, and front matter

ART:
  audiobook_voices.json  — Character → ElevenLabs voice mapping
  landing/index.html     — Responsive landing page template

CONFIG:
  .env.example           — API keys (Anthropic, fal.ai, ElevenLabs)
  pyproject.toml         — Python dependencies

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#How It Works

The novel is five co-evolving layers:

  Layer 5:  voice.md          — HOW we write
  Layer 4:  world.md          — WHAT exists
  Layer 3:  characters.md     — WHO acts
  Layer 2:  outline.md        — WHAT HAPPENS
  Layer 1:  chapters/ch_NN.md — THE ACTUAL PROSE
  Cross-cutting: canon.md     — WHAT IS TRUE

Changes propagate both down (lore change → outline change → chapter revision) and up (writing reveals a gap → update lore → check downstream). The pipeline tracks propagation debts in state.json.

#Two Immune Systems

1. Mechanical (evaluate.py, no LLM): regex scans for banned words, fiction clichés, show-don't-tell violations, sentence uniformity.

2. LLM Judge (evaluate.py, separate model): scores prose quality, voice adherence, character distinctiveness, beat coverage.

#The Opus Review Loop

After automated revision cycles, the full manuscript goes to Claude Opus with this prompt:

"Read the below novel. Review it first as a literary critic and then as a professor of fiction. Give specific, actionable suggestions for any defects you find. Be fair but honest. You don't have to find defects."

The dual-persona review catches what automated tools can't: prose-level repetition, character thinness, ethical gaps, structural monotony. The loop continues until the reviewer's items are mostly qualified hedges rather than real problems.

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#API Keys

The pipeline uses three external services:

Copy .env.example to .env and fill in your keys. Only the Anthropic key is required for the core pipeline. Art and audiobook are optional.

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#Production History

The first novel, The Second Son of the House of Bells, was produced through this pipeline:

• Foundation: World bible, 8 characters, 24-chapter outline, voice discovery
• Drafting: 24 chapters, 75,698 words, sequential with evaluation
• Revision: 6 automated cycles + 6 Opus review rounds
• Structural: 24 → 19 chapters through 4 merges
• Art: Linocut cover (Nano Banana 2), 19 woodcut chapter ornaments (vectorized)
• Audiobook: 19 chapters parsed into 4,179 speaker-attributed segments
• Final: 79,456 words, 6 review rounds, all major items resolved

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#Inspiration

• karpathy/autoresearch — the autonomous research loop
• Brandon Sanderson's writing lectures (Laws of Magic, character sliders)
• K.M. Weiland's Creating Character Arcs
• Blake Snyder's Save the Cat
• Ursula K. Le Guin's "From Elfland to Poughkeepsie"
• slop-forensics and EQ-Bench Slop Score