autonovel

autonovel

Autonomous fantasy-novel pipeline built as a Claude Code project — skills, subagents, and slash commands instead of a Python script. Same modify → evaluate → keep/discard loop, applied to fiction; same five-layer co-evolving structure (voice / world / characters / outline / chapters, plus canon).

Scope. Phases 1–3: foundation → drafting → automated revision → Opus review. Art, audiobook, ePub, landing page, and LaTeX typesetting are out of scope.

⚠️ Token cost. A full run from seed to Phase 3b finish takes several hours and burns serious tokens (Opus review is the heaviest step). Inspect examples/ashen-crown/ first to see what intermediate artifacts look like before committing to a run.

Install

1. Clone

git clone https://github.com/tchr-dev/autonovel.git
cd autonovel

2. Open the directory in Claude Code

claude

3. Pre-approve permissions (optional but recommended)

Suppresses prompts during the multi-hour run:

cp .claude/settings.example.json .claude/settings.local.json

4. Ready

Skills under .claude/skills/ and slash commands under .claude/commands/ are auto-discovered.

No API key is required for the pipeline itself — Claude Code provides the LLM. .env.example is preserved only for users who want to run the standalone Python helpers in scripts/ against the Anthropic API directly.

Quick start

In Claude Code:

/autonovel my-novel-tag

This will:

1. Create novels/my-novel-tag/ and initialise state.
2. Ask for a seed concept (or invoke the seed skill to generate options). The root seed.txt is a drop-in template you can edit.
3. Run Phase 1 (Foundation) until foundation_score > 7.5 AND lore_score > 7.0.
4. Run Phase 2 (Drafting) chapter by chapter until all are above 6.0.
5. Run Phase 3a (Automated revision cycles) until scores plateau.
6. Run Phase 3b (Opus review loop) until no major unqualified items remain.

State persists in novels/<tag>/state.json. Resume with /autonovel-resume <tag>.

Worked example

examples/ashen-crown/ is a real partial run — foundation complete, one chapter drafted, evaluation logs intact. Browse it to see what each phase produces, or copy it into novels/ and resume:

cp -r examples/ashen-crown novels/ashen-crown
# Then in Claude Code:
/autonovel-resume ashen-crown

The seed that produced this run lives at both seed.txt (repo root, as a template) and examples/ashen-crown/seed.txt.

AI-detection results — Chapter 1 (ashen-crown)

The output of this pipeline is built specifically to dodge the structural and lexical tells that fire AI detectors. The first drafted chapter of the worked example was run through two public detectors:

The framework references in framework/ANTI-SLOP.md and framework/ANTI-PATTERNS.md, combined with the regex slop_scan.py pass and the reader-panel evaluation, are what drive these numbers down. Detector scores are not a quality guarantee, but they're a useful sanity check that the prose has avoided the obvious AI fingerprints.

Layout

.claude/
  skills/                       # the LLM-driven steps Claude executes
    autonovel/                  # top-level orchestrator
    seed/
    foundation/                 # gen-world, gen-characters, gen-outline,
                                # gen-canon, voice-discovery
    drafting/                   # draft-chapter
    evaluation/                 # evaluate-{foundation,chapter,full},
                                # adversarial-edit, compare-chapters,
                                # voice-fingerprint
    revision/                   # gen-brief, gen-revision, reader-panel,
                                # opus-review, apply-cuts
  agents/                       # personas spawned as subagents
    panel-{editor,genre-reader,writer,first-reader}.md
    literary-critic.md
    professor-of-fiction.md
  commands/                     # slash commands
    autonovel.md, autonovel-resume.md
  settings.example.json         # pre-approved permissions for unattended runs

scripts/                        # mechanical (no LLM)
  slop_scan.py                  # regex slop scanner
  apply_cuts.py                 # quote-match cut applicator
  voice_fingerprint.py          # quantitative voice analysis
  build_manuscript.py           # concatenate chapters
  parse_review.py               # parse Opus review markdown
  state.py                      # state.json read/update
  results_log.py                # append to results.tsv

framework/                      # immutable craft references
  CRAFT.md
  ANTI-SLOP.md                  # word-level AI tell detection
  ANTI-PATTERNS.md              # structural AI pattern detection
  voice-guardrails.md           # universal voice rules (Part 1)

templates/                      # copied per novel
  voice.md                      # full skeleton (Part 1 + blank Part 2)
  world.md, characters.md, outline.md, canon.md, MYSTERY.md
  state.json

examples/                       # committed worked runs (read-only references)
  ashen-crown/                  # foundation done + 1 chapter drafted

novels/<tag>/                   # one per novel run (gitignored)
  seed.txt
  voice.md, world.md, characters.md, outline.md, canon.md, MYSTERY.md
  chapters/ch_01.md, ch_02.md, …
  arc_summary.md, manuscript.md
  state.json, results.tsv
  edit_logs/                    # adversarial cuts, panel JSON, tournament
  eval_logs/                    # evaluate-* outputs
  reviews/                      # Opus review markdown + parsed JSON
  briefs/                       # revision briefs
  .snapshots/                   # phase-level rollback points

Design notes

• Skills replace LLM-calling Python scripts. No httpx boilerplate, no JSON parsing, no model-name configuration. Claude executes the writing and evaluation directly.
• Subagents replace persona-prompted httpx calls. Each reader-panel persona and each Opus reviewer runs as an isolated subagent context — independent reasoning, no cross-contamination.
• novels/<tag>/ directories replace per-novel git branches. Snapshots are explicit, taken at phase transitions and before risky rewrites. No git reset --hard for "discard"; restore from .snapshots/ instead.
• State is JSON, not git history. Resume from state.json.

Pipeline shape

• The five-layer co-evolving structure (voice / world / characters / outline / chapters + canon)
• Two immune systems: mechanical regex (slop_scan.py) + LLM judge (evaluate-* skills)
• Reader panel of four personas, with consensus / disagreement detection
• Opus dual-persona review loop with stopping conditions
• Adversarial "cut 500 words" pass
• Swiss-tournament Elo ranking of chapters

Hand-running individual skills

# Foundation
gen-world, gen-characters, gen-outline, gen-canon, voice-discovery
evaluate-foundation

# Drafting
draft-chapter (per chapter)
evaluate-chapter

# Revision
adversarial-edit (per chapter or all)
apply-cuts (mechanical)
compare-chapters (tournament)
voice-fingerprint (mechanical)
reader-panel (4 subagents)
gen-brief, gen-revision
opus-review (2 subagents)

Models

Skills assume an Opus-class model is available for Phase 3b review and a Sonnet-class model for drafting/evaluation. On a Haiku-only plan expect degraded prose and weaker review signal.

Subagent model selection is pinned in agent frontmatter rather than inherited from the orchestrator session, so the right model runs on each persona regardless of which model you launched /autonovel from:

For very long manuscripts (≳120k words) where the standard 200k Opus window crowds, switch the two Opus agents to claude-opus-4-7[1m]. This is opt-in per run — the default stays at standard Opus to avoid paying 1M-context rates on the heaviest phase of every pipeline.

License

MIT — see LICENSE.