Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/wordflowlab/novel-writerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/wordflowlab/novel-writer/plan-enhance)<a href="https://agentmods.dev/commands/wordflowlab/novel-writer/plan-enhance"><img src="https://agentmods.dev/badge/commands/wordflowlab/novel-writer/plan-enhance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/wordflowlab/novel-writer/plan-enhance"><img src="https://agentmods.dev/badge/commands/wordflowlab/novel-writer/plan-enhance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00659 |
| Opus 5 | $0.00000 | $0.00329 |
| Sonnet 5 | $0.00000 | $0.00132 |
| Haiku 4.5 | $0.00000 | $0.00066 |
Grade A, and why
plan-enhance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
🆕 可选:动态加载类型知识(genre-knowledge插件)
如果plugins/genre-knowledge/目录存在,你可以动态搜索并应用类型化写作知识。
知识搜索策略
步骤1:识别故事类型
- 从
story.md中提取类型标签(如"玄幻"、"爽文"、"都市重生"等) - 如无明确标签,根据故事内容(金手指、世界观、冲突类型)推断
步骤2:搜索相关知识
使用Glob搜索知识文件(按优先级):
-
通用方法论(所有类型适用):
plugins/genre-knowledge/knowledge/fundamentals/*.md推荐优先阅读:
emotion-value-core-laws.md- 五大商业创作法则scene-mru-structure.md- MRU场景结构(目标-冲突-决定)
-
类型专属知识(根据识别的类型):
plugins/genre-knowledge/knowledge/genres/*{类型关键词}*.md示例:
- 爽文类型:搜索
*shuangwen*.md - 玄幻类型:搜索
*xuanhuan*.md - 都市类型:搜索
*urban*.md
- 爽文类型:搜索
步骤3:知识应用原则
- 🎯 优先级:
类型专属知识 > 通用方法论 > 通用规范 - 📖 选择性应用:快速浏览文件,提取与当前章节规划相关的知识点
- ⚠️ 不要全盘照搬:知识文件是参考,需结合具体故事情况灵活应用
- 🔍 按需搜索:不要一次性加载所有知识,只在需要时搜索相关内容
应用示例
场景:规划一部"都市重生爽文"的开篇3章
搜索知识:
Glob: plugins/genre-knowledge/knowledge/fundamentals/emotion-value-core-laws.md
Glob: plugins/genre-knowledge/knowledge/genres/shuangwen-golden-opening.md
提取要点:
- 从五大法则中应用"极限铺垫原则"(开篇构建困境)
- 从黄金开篇中应用"动态场景切入"和"核心冲突前置"
规划应用:
- 第1章:动态场景切入重生瞬间,立刻展现主角困境
- 第2章:简要揭示重生金手指(核心信息差)
- 第3章:完成第一个小逆袭(打脸爽点)
注意事项
- ⚠️ 知识文件基于商业网文创作经验,不适合所有类型
- ⚠️ 不要让知识约束创意,保持故事的独特性
- ⚠️ 如果故事不属于商业类型文,可以跳过此部分
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 63 lines · 0 tokens per session scan A 752a2326c8ee
plan-enhance is a command published in the GitHub repository wordflowlab/novel-writer (943 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 659 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.