oh-story-claudecode
Automates the full pipeline for Chinese web novel production—from competitive analysis to final cover generation—reducing manual workflow overhead and mainta…
A Chinese web-novel (网文) writing toolkit of 13 skills covering the full production pipeline for long- and short-form serialized fiction: scanning bestseller rankings for trends, deconstructing hit chapters, drafting outlines and prose, removing AI-writing tells (去AI味), and generating cover art. Built for Chinese web-novel authors and publishing teams; works with Claude Code, OpenCode, OpenClaw, and Codex CLI. Running /story-setup deploys specialized agents and hooks into a writing project.
- Analyze bestseller lists to identify trending tropes and market gaps
- Deconstruct high-performing chapters to extract narrative structures
- Remove AI-detection markers from generated or revised text
Automates the full pipeline for Chinese web novel production—from competitive analysis to final cover generation—reducing manual workflow overhead and maintaining consistent output quality.
Chinese web novel writers and publishing teams managing multiple titles or serial releases who need systematic processes for research, editing, and asset creation.
https://github.com/worldwonderer/oh-story-claudecode
By worldwonderer
How to Get It
npx skills add worldwonderer/oh-story-claudecode -y -g
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
After installing, paste this into Claude:
Help me analyze bestseller lists to identify trending tropes and market gaps
Trust Signals Auto-scanned
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 1,347 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 1,347 GitHub stars; contributors unknown; last commit 0d ago; license MIT.