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 devcxl/mattpocock-skills-zh --skill to-questionnairegit clone --depth 1 https://github.com/devcxl/mattpocock-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/devcxl/mattpocock-skills-zh/to-questionnaire)<a href="https://agentmods.dev/skills/devcxl/mattpocock-skills-zh/to-questionnaire"><img src="https://agentmods.dev/badge/skills/devcxl/mattpocock-skills-zh/to-questionnaire/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/devcxl/mattpocock-skills-zh/to-questionnaire"><img src="https://agentmods.dev/badge/skills/devcxl/mattpocock-skills-zh/to-questionnaire.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.00738 |
| Opus 5 | $0.00012 | $0.00369 |
| Sonnet 5 | $0.00005 | $0.00148 |
| Haiku 4.5 | $0.00002 | $0.00074 |
Grade A, and why
to-questionnaire 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.
What it actually says
转化为问卷
将用户无法独自回答的问题转化为一份问卷：一份 Markdown 文档，交给某个人异步填写，或在会议中共同填写。接收者拥有用户缺乏的知识；问卷负责把这些知识挖掘出来。
审问"发送"而非"主题"。 只询问用户关于"发送"的问题：这是他们总能回答的：发给谁，以及需要什么回来。文档中的问题则针对接收者所知与用户所需之间的差距。
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发给谁？ 用一个来回问清楚接收者的角色、专长，以及和用户的关系。这决定了问卷的语气和需要携带多少上下文。完成条件：你知道了接收者是谁，以及他们知道什么用户不知道的东西。
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你需要什么回来？ 用一个来回问清楚用户无法独自解决、需要从这个人那里获得的具体决策或事实。完成条件：你有了一个具体的清单，列出用户必须能据此做出或决定的事情。
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撰写问卷。 针对步骤 1-2 中确定的差距起草问题，遵循下面的文档结构。将问卷写入当前目录下的
to-questionnaire-<slug>.md文件（slug 来自主题），并报告文件路径。完成条件：文件存在，且用户在步骤 2 中提到的每一个项目都有对应的问题覆盖。
文档结构
将文档定位为一份发现问卷：用户缺乏上下文，接收者掌握它。按重要性排序：异步意味着你可能只有一次机会：当问题超过几个时，用 ## 标题按主题分组。使用下面的模板编写。
<问卷标题>
目的： 此问卷存在的原因以及它所影响的决策。
来自： <用户> ： 发给： <接收者> ： 你的回答将用于： <去向>
背景
一段话，为不了解用户背景的接收者提供上下文。足以让他们回答好问题，不超过一页。
如何回答
截止日期和大致的投入时间。部分回答和"我不知道"同样有用：如果你对任何内容不确定，请标注出来，而不是跳过它。
<主题标题>
每个主题一个 ## 部分。每个部分下按重要性排序的问题。每个问题只有一个想法：绝不复合：每个问题下方直接留有回答位置，并且仅在问题可能被误解或导致敷衍回答时才添加一行_为什么这很重要_。
为什么这很重要：它决定了我们是现在为突发流量做准备，还是推迟到以后。
还有其他问题吗？
一个开放的收尾问题：我们没问到但你应该告诉我们的任何事？
What ships with it
1 file 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 · 56 lines · 23 tokens per session scan A 7f20403e6d08
to-questionnaire is a skill published in the GitHub repository devcxl/mattpocock-skills-zh (340 stars, last pushed 6d ago), licensed MIT. It adds 23 tokens to every session and 738 once invoked, about $0.0001 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
chinese-documentation
A Chinese technical-documentation style guide covering spacing, punctuation, numbers, terminology, and links when Chinese and English appear together.
chinese-code-review
A Chinese-language code-review communication guide with templates and severity levels for review comments.
chinese-commit-conventions
A Chinese-language guide to Conventional Commits, a format for writing consistent Git commit messages, plus related changelog, commit-checking, and commit-helper configuration.
systematic-debugging
A step-by-step method for finding the underlying cause of technical problems before changing code. It covers reading errors, reproducing failures, checking recent changes, and tracing data across system components.
dispatching-parallel-agents
A workflow for assigning separate coding agents to independent tasks that do not share state or depend on one another. Each agent receives a focused scope, constraints, and an expected result.
executing-plans
A procedure for carrying out a written software implementation plan in a separate session, including reviewing the plan, completing its tasks, and checking the results at defined review points.