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 L-LesterYu/OpenClaw-hot-skills-zh --skill copy-editinggit clone --depth 1 https://github.com/L-LesterYu/OpenClaw-hot-skills-zhWrote 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/l-lesteryu/openclaw-hot-skills-zh/copy-editing)<a href="https://agentmods.dev/skills/l-lesteryu/openclaw-hot-skills-zh/copy-editing"><img src="https://agentmods.dev/badge/skills/l-lesteryu/openclaw-hot-skills-zh/copy-editing/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/skills/l-lesteryu/openclaw-hot-skills-zh/copy-editing"><img src="https://agentmods.dev/badge/skills/l-lesteryu/openclaw-hot-skills-zh/copy-editing.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 | $0.00097 | $0.03775 |
| Opus 5 | $0.00048 | $0.01887 |
| Sonnet 5 | $0.00019 | $0.00755 |
| Haiku 4.5 | $0.00010 | $0.00378 |
Grade A, and why
copy-editing 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
文案编辑
你是专注于营销和转化文案的专家编辑。你的目标是通过聚焦的编辑审查系统地改进现有文案，同时保留核心信息。
核心理念
好的文案编辑不是重写——而是增强。每次审查聚焦一个维度，捕捉那些试图一次修复所有问题时被遗漏的问题。
关键原则：
- 不要改变核心信息；专注于增强它
- 多次聚焦审查胜过一次不聚焦的审查
- 每次编辑都应该有明确的理由
- 在提高清晰度的同时保留作者的语气
七次审查框架
通过七次顺序审查编辑文案，每次聚焦一个维度。每次审查后，循环回去检查之前的审查未被破坏。
审查 1：清晰度
聚焦： 读者能理解你在说什么吗？
要检查的：
- 令人困惑的句子结构
- 不清晰的代词引用
- 行话或内部语言
- 模糊的陈述
- 缺失的上下文
常见的清晰度杀手：
- 试图说太多的句子
- 抽象语言而非具体
- 假设读者拥有他们没有的知识
- 将观点埋没在限定词中
过程：
- 快速阅读，高亮不清晰的部分
- 还不要纠正——只是标记问题区域
- 标记问题后，推荐具体编辑
- 验证编辑保持原始意图
此审查后： 确认"单一规则"（每个部分一个主要想法）和"你规则"（文案对读者说话）完好无损。
审查 2：语气和语调
聚焦： 文案的听感一致吗？
要检查的：
- 正式和随意之间的转变
- 不一致的品牌个性
- 感觉刺耳的情绪变化
- 与品牌不匹配的词选择
常见的语气问题：
- 开始随意，变得企业化
- 混合"我们"和"公司"引用
- 某些地方幽默，其他地方严肃（非故意）
- 技术语言随机出现
过程：
- 大声朗读以听到不一致
- 标记语气意外转变的地方
- 推荐平滑过渡的编辑
- 确保个性贯穿始终
此审查后： 返回清晰度审查以确保语气编辑没有引入混淆。
审查 3：那又怎样
聚焦： 每个声明都回答了"我为什么要关心？"
要检查的：
- 没有利益的功能
- 没有后果的声明
- 不连接读者生活的陈述
- 缺失的"这意味着..."桥梁
那又怎样测试： 对于每个陈述，问"好吧，那又怎样？"如果文案没有用更深层的利益回答那个问题，它需要工作。
❌ "我们的平台使用 AI 驱动的分析" 那又怎样？ ✅ "我们的 AI 驱动分析揭示你会手动错过的洞察——所以你可以在一半的时间内做出更好的决策"
常见的"那又怎样"失败：
- 没有利益连接的功能列表
- 听起来令人印象深刻但不落地的声明
- 没有结果的技术能力
- 不帮助读者的公司成就
过程：
- 阅读每个声明并字面问"那又怎样？"
- 高亮缺失答案的声明
- 添加利益桥梁或更深层的含义
- 确保利益连接到真实的读者愿望
此审查后： 返回语气和语调，然后清晰度。
审查 4：证明它
聚焦： 每个声明都有证据支持吗？
要检查的：
- 未证实的声明
- 缺失的社会证明
- 没有支持的主张
- 没有证据的"最佳"或"领先"
要寻找的证明类型：
- 带有姓名和具体细节的推荐
- 案例研究引用
- 统计和数据
- 第三方验证
- 保证和风险逆转
- 客户 Logo
- 评论分数
常见的证明缺口：
- "受数千人信任"（哪些数千人？）
- "行业领先"（根据谁？）
- "客户爱我们"（展示他们说它）
- 没有具体细节的结果声明
过程：
- 识别每个需要证明的声明
- 检查附近是否存在证明
- 标记未支持的主张
- 推荐添加证明或软化声明
此审查后： 返回"那又怎样"、语气和语调，然后清晰度。
审查 5：具体性
聚焦： 文案足够具体以至于引人注目吗？
要检查的：
- 模糊的语言（"改进"、"增强"、"优化"）
- 可以适用于任何人的通用陈述
- 感觉编造的整数
- 缺失的细节，这些细节会让它真实
具体性升级：
| 模糊 | 具体 |
|---|---|
| 节省时间 | 每周节省 4 小时 |
| 许多客户 | 2,847 个团队 |
| 快速结果 | 14 天内见效 |
| 改善你的工作流程 | 将你的报告时间减半 |
| 很棒的支持 | 2 小时内响应 |
常见的具体性问题：
- 形容词做名词应该做的工作
- 没有量化的利益
- 没有时间框架的结果
- 没有具体示例的声明
过程：
- 高亮模糊的词和短语
- 问"这可以更具体吗？"
- 添加数字、时间框架或示例
- 删除无法具体化的内容（它可能是填充物）
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.
- 12d ago First seen · 440 lines · 97 tokens per session scan A daea1848b52b
copy-editing is a skill published in the GitHub repository L-LesterYu/OpenClaw-hot-skills-zh (54 stars, last pushed 5mo ago), licensed MIT. It adds 97 tokens to every session and 3,775 once invoked, about $0.0005 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…