Disclosure: This is my project. It’s free (CC BY-NC-SA 4.0). No cost, no paywall, no affiliate links. I’m the author. I’m sharing it because the Claude Code workflow might be interesting to this community. What it is: A hard sci-fi novel called Checkpoint — 30 chapters, ~123,000 words, set in 2041. BCIs adopted by 900M people. The device reads the brain. It also writes to it. Four POVs across four continents. What the Claude Code pipeline looked like: Research & concept: World-building bible, character sheets, chapter outlines — all generated collaboratively in Claude, iterated through feedback loops. Writing: Chapter-by-chapter generation from the outline. Each chapter drafted, reviewed, revised in conversation. Markdown source files, git-tracked from day one. Editing — this is where Claude Code shined: Dispatched 5 parallel review agents across all 30 chapters to find inconsistencies, factual errors, clunky phrasing, and AI-writing tics Found ~50 issues: 60Hz power hum in Germany (should be 50Hz), wrong football club, character nationality contradicting between chapters, a psychiatrist called a surgeon Style pass: identified “the way [X] [verbed]” appearing 100+ times — the novel’s biggest AI-writing tell. Cut ~45% across 30 chapters using parallel agents Prose tightening: 143K → 123K words. One agent batch cut a chapter by 52% (had to git checkout HEAD and redo with stricter constraints in the prompt) Build pipeline: build.sh — pandoc + xelatex → PDF build-reader.sh — markdown → single-page HTML reader build-audiobook.sh — markdown → ElevenLabs-ready plain text deploy.sh — builds everything + FTP syncs to production server via lftp One-command deploy: ./deploy.sh rebuilds all formats from the markdown source and pushes to the live site. What I learned about Claude Code for long-form creative work: Parallel agents are powerful but need constraints. “Cut 10-15%” without a hard ceiling led to 52% cuts. “STRICT 10%. Do NOT exceed 15% on any chapter” worked. Consistency across 30 chapters is hard. Names, ages, timelines, device model numbers, even the Hz of fluorescent lights — all drifted. Dedicated consistency-check agents were essential. The 1M context window matters. Earlier models couldn’t hold the full novel. Opus 4.6 with 1M context could cross-reference chapters in a single pass. Review > generation. The writing was fast. Finding what was wrong — factual errors, style tics, logical inconsistencies, cultural false notes — took 3x longer. Repo: https://github.com/batmanvane/checkpointnovel Live: https://checkpoin.de/ (read online, PDF, audiobook) submitted by /u/rueckstauklappe
Originally posted by u/rueckstauklappe on r/ClaudeCode