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/wangyu-style)<a href="https://agentmods.dev/commands/wordflowlab/novel-writer/wangyu-style"><img src="https://agentmods.dev/badge/commands/wordflowlab/novel-writer/wangyu-style/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/wangyu-style"><img src="https://agentmods.dev/badge/commands/wordflowlab/novel-writer/wangyu-style.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 | $0.00763 |
| Opus 5 | $0.00000 | $0.00381 |
| Sonnet 5 | $0.00000 | $0.00153 |
| Haiku 4.5 | $0.00000 | $0.00076 |
Grade A, and why
wangyu-style 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 11d 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
激活忘语风格创作模式
设定并激活忘语风格(《凡人修仙传》作者)创作模式,加载完整的风格参数和世界观体系。
使用方法
/wangyu-style [选项]
选项
--intensity <1-10>- 风格强度(默认:8)--focus <aspect>- 重点强化某个方面:world- 世界观构建character- 人物塑造suspense- 悬念设置resource- 资源体系battle- 战斗描写
功能说明
激活后将自动:
-
加载风格模板
- 冷静客观的叙述风格
- 严谨的修仙世界观
- 复杂的人物关系网
-
初始化世界设定
- 八大修炼境界体系
- 四类修仙资源系统
- 多元势力格局
-
配置创作参数
- 叙事距离:疏离客观
- 情感强度:克制内敛
- 描写密度:详实精准
- 悬念等级:层层递进
-
启用特色功能
- 伏笔追踪系统
- 资源稀缺性检查
- 力量体系验证
- 人物行为逻辑审核
示例
用户:/wangyu-style
AI:忘语风格模式已激活!
已加载设定:
✓ 修仙世界观体系(炼气→大乘八境)
✓ 资源稀缺性系统(灵石、丹药、法宝、功法)
✓ 冷静客观叙事风格
✓ 谨慎务实主角模板
✓ 悬念伏笔追踪器
风格特征已启用:
- 叙述保持冷静疏离感
- 强调资源获取的艰难
- 人物关系利益交织
- 战斗注重策略和细节
- 自动埋设长线伏笔
您可以开始创作了!建议使用 /wangyu-write 命令开始写作。
用户:/wangyu-style --intensity 10 --focus battle
AI:忘语风格模式已激活!(最高强度·战斗特化)
特别强化:
✓ 斗法场面的宏大描写
✓ 法术细节的精确刻画
✓ 法宝运用的策略性
✓ 生死搏杀的紧张感
✓ 战斗节奏的把控
已为您优化战斗描写相关的所有参数。
注意事项
- 本模式特别适合仙侠、玄幻类题材
- 建议配合
/track命令管理伏笔 - 可与其他写作方法(如英雄之旅)结合
- 激活后会影响所有后续创作命令
相关命令
/wangyu-write- 使用忘语风格写作/wangyu-analyze- 分析风格符合度/wangyu-enhance- 强化忘语风格特征/expert wangyu-master- 召唤忘语风格大师
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.
- 11d ago First seen · 96 lines · 0 tokens per session scan A a19aff3d630c
wangyu-style is a command published in the GitHub repository wordflowlab/novel-writer (944 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 763 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.