Borrowing it
Nothing to install: this file belongs to Korck-lab/deepseek-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Korck-lab/deepseek-mcp/main/.agents/skills/to-questionnaire/SKILL.mdgit clone --depth 1 https://github.com/Korck-lab/deepseek-mcpWrote 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/korck-lab/deepseek-mcp/to-questionnaire)<a href="https://agentmods.dev/skills/korck-lab/deepseek-mcp/to-questionnaire"><img src="https://agentmods.dev/badge/skills/korck-lab/deepseek-mcp/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/korck-lab/deepseek-mcp/to-questionnaire"><img src="https://agentmods.dev/badge/skills/korck-lab/deepseek-mcp/to-questionnaire.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 | $0.00021 | $0.00633 |
| Opus 5 | $0.00010 | $0.00316 |
| Sonnet 5 | $0.00004 | $0.00127 |
| Haiku 4.5 | $0.00002 | $0.00063 |
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 10d 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.
This is a copy
97% identical to to-questionnaire — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Turn something the user can't answer alone into a questionnaire — a Markdown document they hand to one person to fill in async, or fill out together over a meeting. The recipient holds knowledge the user lacks; the questionnaire pulls it out of them.
Grill the send, not the subject. Interview the user only about the send, which they can always answer: who it goes to, and what they need back. The questions in the document then target the gap between what the recipient knows and what the user needs.
-
Who is it going to? Ask, in one exchange, the recipient's role, expertise, and relationship to the user. This fixes the questionnaire's tone and how much context it must carry. Done when you know who the recipient is and what they know that the user doesn't.
-
What do you need back? Ask, in one exchange, the specific decisions or facts the user can't resolve alone and needs from this person. Done when you have a concrete list of what the user must walk away able to do or decide.
-
Write the questionnaire. Draft questions aimed at the gap from steps 1–2, following the Document structure below. Write it to
to-questionnaire-<slug>.mdin the current directory (slug from the topic) and report the path. Done when the file exists and every item the user named in step 2 is covered by a question.
Document structure
Frame the document as a discovery questionnaire: the user lacks context, the recipient holds it. Order questions most-important-first — async means you may only get one pass — and group them under ## headings by theme once there are more than a handful. Write it using the template below.
Purpose: why this questionnaire exists and the decision riding on it.
From: — To: — How your answers will be used:
Context
One paragraph orienting a recipient who wasn't in the user's head. Enough to answer well, not a page.
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.
- 10d ago First seen · 54 lines · 21 tokens per session scan A 8e7f9ed8d7b2
to-questionnaire is a skill published in the GitHub repository Korck-lab/deepseek-mcp (0 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 633 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to to-questionnaire, differing in 11 lines, and is treated as a copy.
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