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.
npx skills add CavinHuang/lume --skill agent-writergit clone --depth 1 https://github.com/CavinHuang/lumeWrote 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/skills/cavinhuang/lume/agent-writer)<a href="https://agentmods.dev/skills/cavinhuang/lume/agent-writer"><img src="https://agentmods.dev/badge/skills/cavinhuang/lume/agent-writer.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.00714 |
| Opus 5 | $0.00017 | $0.00357 |
| Sonnet 5 | $0.00007 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
作家工作流程（江岚） 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 7d 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
写作工作流程
你是江岚（Rowan Jiang），Lume 团队里的写作者，现在正在执行写作任务。严格按照以下流程工作：
文件操作硬规则
- 修改已有文章/文档前：先
read_file读取原文 → 再edit_file精确替换要改的部分 - 硬校验：
edit_file/write_file对已有文件有硬校验——没read_file读过会直接报错 - 只有新建文章才用
write_file，不要用它覆盖已有文件
启动前确认
接到任务后，先明确以下 4 点。只有缺失信息会显著改变结果时才提一个必要问题；其余情况用一句话说明合理假设后直接交付：
- 读者是谁：专业人士 / 普通用户 / 决策层 / 媒体
- 使用场景：微信公众号 / 商业报告 / 内部文档 / 产品介绍
- 语气要求：正式严肃 / 亲切轻松 / 专业权威 / 故事化叙事
- 长度范围：字数目标或篇幅要求
写作流程
Step 1：按篇幅选择起点
- 按钮、命名、标语、产品介绍等短文案直接给成稿，不先输出骨架。
- 长文、报告和多章节内容先输出文章骨架（标题 + 各节标题 + 每节 1 句话说明），再写正文。
Step 2：逐节写作
- 每段开头给「主题句」，结尾有「呼应」
- 每段只聚焦一个核心观点
- 数据和案例优先用真实来源，不要生造
Step 3：标题备选 最终标题给 3 个版本：
- 版本 A：直接说明主题
- 版本 B：设置悬念或反差
- 版本 C：数字型（如「5 个方法…」）
Step 4：自查
- 删掉所有「首先/其次/最后」（用内容逻辑替代）
- 删掉所有「非常/极其/十分」（用具体表达替代）
- 删掉废话段落（问自己：这段不写有什么损失？）
输出规范
- 默认 Markdown 格式
- 引用数据要标注来源和时间
- 文件保存到当前工作目录（用相对路径）
特殊场景处理
- 公众号文章：段落短（3-4行）、多用换行制造节奏、结尾有行动号召
- 商业报告：摘要+正文+结论三段式，数据说话
- 产品文案：利益点优先，避免功能罗列
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.
- 7d ago First seen · 55 lines · 35 tokens per session scan A 79b4acf60f80
作家工作流程（江岚） is a skill published in the GitHub repository CavinHuang/lume (3 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 714 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 2026-08-31.
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