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 Haochenhust/ch-skills --skill meta-promptgit clone --depth 1 https://github.com/Haochenhust/ch-skillsWrote 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/haochenhust/ch-skills/meta-prompt)<a href="https://agentmods.dev/skills/haochenhust/ch-skills/meta-prompt"><img src="https://agentmods.dev/badge/skills/haochenhust/ch-skills/meta-prompt/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/haochenhust/ch-skills/meta-prompt"><img src="https://agentmods.dev/badge/skills/haochenhust/ch-skills/meta-prompt.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 | $0.00144 | $0.03283 |
| Opus 5.5 | $0.00058 | $0.01313 |
| Sonnet 5.5 | $0.00029 | $0.00657 |
| Haiku 4.5 | $0.00014 | $0.00328 |
Grade A, and why
meta-prompt 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 2mo 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta-Prompt Skill
定位与边界
做什么：接收用户一段原始任务描述（可以是一句话到一大段话），通过最少的澄清问题 + 按需叠加的 prompt engineering 技巧，输出一段完整、结构化、可直接复制到其他 AI 会话使用的高质量 prompt。
不做什么（严格）：
- 不执行产出的 prompt。用户要的是 prompt 文本本身，不是任务结果。哪怕产出的 prompt 看起来很简单、哪怕你以为顺手做掉更贴心——都不要做。这是这个 skill 最容易踩的坑。
- 不做面向产品的 prompt 工程（安全护栏、A/B 评估、版本管理、一致性测试都不在范围内）。
- 不覆盖长任务规划（那是
writing-plans的范畴，如果用户需求明显是多步实施规划，建议他们用 writing-plans）。
为什么：用户常常在对话里"不得要领"——AI 理解不到重点、格式不对、视角不对。根因 90% 是上下文缺失和约束模糊，不是模型不够强。这个 skill 把"帮用户把任务表达清楚"这件事做好，剩下的交给目标 AI。
全程中文
与用户的所有交互（澄清问题的措辞、技巧说明、交付说明）都用中文。产出的 prompt 本身按目标 AI 使用场景的自然语言——用户任务描述是中文就用中文写 prompt，是英文就用英文。
工作流（严格按顺序）
Step 1 — 意图解析
读入用户原始输入，按以下 7 维度扫描，心里给每一维标记 ✓ 已知 / ✗ 缺失 / ⚠️ 歧义：
| 维度 | 判断要点 |
|---|---|
| 目标 | 用户最终想拿到什么可交付物？ |
| 受众/使用场景 | 产物给谁看、用在什么场景？ |
| 输入数据或上下文 | 有没有素材要喂给目标 AI？（文本、数据、代码…） |
| 约束条件 | 字数、风格、语言、禁用项、必须包含项 |
| 输出格式 | 文本/JSON/列表/表格/代码/markdown 分节… |
| 成功标准 | 什么样算好？用户隐含的质量门槛 |
| 目标 AI | Claude（默认）/ GPT / Gemini / 其他 |
为什么这 7 维：这是勾勒一个任务的最小充分集。少一维 prompt 就会漏信息，多一维就是过度工程。
Step 2 — 动态澄清（用 AskUserQuestion）
只问 ✗ 缺失 / ⚠️ 歧义的维度，一次性批量用 AskUserQuestion 工具提出：
- 问题数量：1–4 个。超过 4 说明你还没把原始输入榨干，先回 Step 1 再读一遍。
- 每题给 3–4 个具体、可选中的选项 + "其他（请说明）"。不要给"详细"/"简洁"这种形容词级选项，要给"<200 字简报"/"300-500 字"/"1 页以上详细"这种有量化锚点的选项。
- 不要问已经明确的维度。用户输入里"写个 Python 函数"已经暗示代码输出格式了，就别再问。
- 原始输入已经非常清晰时（7 维里至少 5 维 ✓），直接跳过 Step 2。
为什么澄清而不改写：用户的日常痛点是"AI 没理解我"，90% 是上下文缺失，10% 是表达问题。澄清直接补上下文，改写只在语言层面化妆。
Step 3 — 技巧选择
根据任务画像从下列菜单挑选技巧。不是越多越好——每个技巧都要能说出"为什么选它"。索引如下，详细模板和触发条件见 references/techniques.md（需要具体模板片段时再读）：
| 技巧 | 一句话触发条件 |
|---|---|
| Role assignment | 任务从特定领域视角做会明显更好（技术/法律/编辑/教师等） |
| Chain-of-Thought | 涉及多步推理、计算、决策、因果分析 |
| Few-shot examples | 格式/风格很特殊，光描述说不清，样例能一秒说清 |
| Self-consistency | 答案方差大的开放题，让模型生成 N 个候选再挑 |
| Step-back prompting | 需要先抽象原则再套具体案例，避免模型一头扎进细节 |
| Chain-of-Verification | 事实性输出，容易幻觉的话题（历史、数据、引用） |
| Decomposition | 任务天然有多个子可交付物，拆开回答质量更高 |
| XML 结构化标签 | Claude 默认开启，多字段输入、长上下文时尤其关键 |
| Markdown 分节 | GPT 默认用，GPT 对 markdown 标题更敏感 |
| Output schema | 产出要被程序解析（JSON、CSV、固定表格结构） |
| Negative constraints | 有明确的失败模式要避免（"不要 xxx"） |
| Delimiter 隔离 | 用户数据里可能混入貌似指令的内容 |
What ships with it
2 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.
- 2mo ago First seen · 254 lines · 144 tokens per session scan A 126069371eda
meta-prompt is a skill published in the GitHub repository Haochenhust/ch-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 144 tokens to every session and 3,283 once invoked, about $0.0006 per session on Opus 5.5. 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.
meta-prompt compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| meta-promptHaochenhust/ch-skills | Skill | 9 | 3mo ago | MIT | A | 144 | Claude Code, Codex |
| prompt-masternidhinjs/prompt-master | Skill | 16k | 15d ago | MIT | B | 78 | Claude Code, Codex |
| Prompt RefinerNotysoty/openagentskills | Skill | 9 | 2mo ago | MIT | A | 21 | Claude Code, Codex |
| thanosthanospapage/thanos | Skill | 2 | 1mo ago | MIT | A | 134 | Claude Code |
| encargostgomoyaa/encargo | Skill | 2 | 3mo ago | MIT | A | 105 | Claude Code, Codex |
| ai-engineering-toolkitlingxling/awesome-skills-cn | Skill | 329 | 1mo ago | MIT | A | 47 | Claude Code |
| prompt-coachgoingli0324/prompt-coach | Skill | 1 | 3mo ago | MIT | A | 154 | Claude Code, Codex |