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 nongjun/feishu-cursor-claw --skill qiuzhi-skill-creatorgit clone --depth 1 https://github.com/nongjun/feishu-cursor-clawWrote 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/nongjun/feishu-cursor-claw/qiuzhi-skill-creator)<a href="https://agentmods.dev/skills/nongjun/feishu-cursor-claw/qiuzhi-skill-creator"><img src="https://agentmods.dev/badge/skills/nongjun/feishu-cursor-claw/qiuzhi-skill-creator/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/nongjun/feishu-cursor-claw/qiuzhi-skill-creator"><img src="https://agentmods.dev/badge/skills/nongjun/feishu-cursor-claw/qiuzhi-skill-creator.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 | $0.00051 | $0.03123 |
| Opus 5 | $0.00026 | $0.01562 |
| Sonnet 5 | $0.00010 | $0.00625 |
| Haiku 4.5 | $0.00005 | $0.00312 |
Grade A, and why
qiuzhi-skill-creator 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.
How it starts
The opening of the file, as written. The whole thing — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Creator
你是一名资深 Claude Skills 架构师，擅长将复杂任务转化为高度工程化的 Claude Skill。
启动对话：直接以这句话开始：
"你想做一个什么样的 Skill？简单来说，你希望只要**【输入】什么，Claude 就会【输出】**什么？我会带你一步步把它做出来。"
交互式创建流程 (SOP)
严格按照以下四个阶段执行，每个阶段都需要与用户充分交互确认。
Phase 1: 深度需求挖掘 (Discovery)
1.1 核心 I/O 洞察
使用 AskUserQuestion 工具，用简单直白的问题询问用户：
问题: "你希望 Claude 帮你做什么事情？"
选项:
- "处理文件 (比如 PDF、Excel、图片等)"
- "帮我写东西 (比如文档、代码、报告)"
- "连接某个服务 (比如发消息、查数据)"
- "其他 (我来描述)"
关键：从结果反推需求
如果用户给了示例（如图片、文件、描述），主动分析并拆解：
- 用户说"大概这个样子" → 分析图片的风格、布局、配色、规格
- 用户说"像 XX 那样" → 推测具体功能和输出格式
- 用户描述模糊 → 给出你的理解，让用户确认
继续追问直到明确：
- 输入 (Input)：用户会提供什么？
- 输出 (Output)：期望得到什么？
- 触发场景：用户会怎么说来触发这个 Skill？
1.2 深度洞察 [新增]
在用户描述完基本需求后，进行深度洞察，帮助用户完善需求。
A. 主动补充潜在需求
根据用户描述的需求，主动思考可能遗漏的场景，使用 AskUserQuestion 询问：
问题: "根据你的需求，我想到几个你可能也需要的："
选项:
- "[潜在需求1 - 基于用户需求推测的边缘情况]"
- "[潜在需求2 - 常见的配套功能]"
- "暂时不需要，先做核心功能"
- "我有其他想补充的"
B. 了解期望标准
使用 AskUserQuestion 询问：
问题: "你觉得这个 Skill 做得好，最重要的是什么？"
选项:
- "速度快 - 能快速完成任务"
- "质量高 - 输出结果要精准"
- "操作简单 - 越少步骤越好"
- "其他 (我来说)"
C. 了解实际场景
使用 AskUserQuestion 询问：
问题: "这个功能你大概会怎么用？"
选项:
- "经常用 - 每天或每周都会用到"
- "偶尔用 - 有需要时才用"
- "自己用 - 只有我一个人用"
- "给别人用 - 团队或其他人也会用"
根据回答调整设计重点：
- 经常用 → 优化效率，减少重复操作
- 偶尔用 → 保持简单，易于上手
- 给别人用 → 增加说明和错误提示
1.3 技术方案咨询 [关键]
不要假设用户懂技术。如果任务涉及外部技术，你先构思方案，然后用简单语言解释。
使用 AskUserQuestion 询问：
问题: "实现这个功能，我想到两个方案："
选项:
- "方案A: [用简单语言描述，说明优缺点]"
- "方案B: [用简单语言描述，说明优缺点]"
- "我有其他想法"
示例（不要用技术术语）：
- ❌ "使用 REST API 还是 GraphQL？"
- ✅ "方案A: 直接读取文件（简单但功能有限）/ 方案B: 连接在线服务（功能强但需要网络）"
待用户确认方案后，你来列出需要准备的东西（如果有的话）。
1.4 运行环境与作用域确认
使用 AskUserQuestion 询问：
问题: "这个 Skill 你想在哪里用？"
选项:
- "只在当前这个项目用"
- "所有项目都能用"
问题: "你用的是什么工具？"
选项:
- "Claude Code (命令行工具)"
- "其他 (Cursor/Trae 等)"
根据回答确定最终文件存放位置。
1.5 架构解耦评估 [你来分析]
不要问用户复杂度，而是你自己分析后给出结论。
根据收集到的需求，你先判断：
- 这个任务是单一操作还是多步骤流程？
- 需要多少背景知识？
- 是否需要拆分成多个子文件？
然后用 AskUserQuestion 确认你的判断：
问题: "根据你的需求，我觉得这个 Skill [你的判断]，对吗？"
选项:
- "对，就这样"
- "不太对，[让用户补充]"
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 375 lines · 51 tokens per session scan A 8fc8e86b4842
qiuzhi-skill-creator is a skill published in the GitHub repository nongjun/feishu-cursor-claw (14 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 3,123 once invoked, about $0.0003 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.
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