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/authenticity-audit)<a href="https://agentmods.dev/commands/wordflowlab/novel-writer/authenticity-audit"><img src="https://agentmods.dev/badge/commands/wordflowlab/novel-writer/authenticity-audit/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/authenticity-audit"><img src="https://agentmods.dev/badge/commands/wordflowlab/novel-writer/authenticity-audit.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 | $0.00034 | $0.00531 |
| Opus 5 | $0.00017 | $0.00266 |
| Sonnet 5 | $0.00007 | $0.00106 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
authenticity-audit 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- authenticity-audit — 100% identical, 0 lines differ
What it actually says
人味自查与改写
目标：帮助作者发现文本中的模板化痕迹、空话与节奏单一的问题，并提供面向行/段落的微调建议。强调“更像你写的”，而非追求任何形式的检测规避
使用方法
/authenticity-audit
[粘贴需要检查的文本]
检查维度（给出计数与示例）
若存在 spec/knowledge/audit-config.json，以其中的连接词、空话清单、句长阈值为准；否则使用内置默认规则
- 模板连接词密度：如“首先、其次、再者、总而言之”等的出现次数与替换建议
- 空泛表达：如“在某种程度、众所周知、众多领域”等，给出更具体的改写
- 句长方差：统计平均句长与方差，标记“连续长句”或“连续短句”的片段，并给出节奏打断/合并建议
- 陈词滥调：识别可替换的套话，提供两到三种更贴近内容的改写版本
- 抽象过载：提示哪些段落抽象名词密度过高，并建议补充可感知的细节
- 人设一致性：对比
.specify/memory/personal-voice.md中的口头禅/词汇，标记缺失的“个体信号”并建议自然融入点
改写输出
- 分段输出：原句 → 问题简述 → 微调版 → 更大胆的个人化版本
- 控制幅度：优先微调，尽量保留原意；仅在必要时提供更大幅度的改写
- 体例保持：不改变原文的文体与叙述角度，除非用户明确请求
末尾建议卡片
- 给出 5-8 条“下次写作时可直接使用”的个体化提示，来源于本次文本暴露的问题
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 · 38 lines · 34 tokens per session scan A b60d4ef37a11
authenticity-audit is a command published in the GitHub repository wordflowlab/novel-writer (943 stars, last pushed 10mo ago), licensed MIT. It adds 34 tokens to every session and 531 once invoked, about $0.0002 per session on Opus 5. 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.