Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add melodic-software/claude-code-plugins/plugin install planningWrote 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/melodic-software/claude-code-plugins/questionnaire)<a href="https://agentmods.dev/skills/melodic-software/claude-code-plugins/questionnaire"><img src="https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/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/melodic-software/claude-code-plugins/questionnaire"><img src="https://agentmods.dev/badge/skills/melodic-software/claude-code-plugins/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 | $0.00151 | $0.01201 |
| Opus 5.5 | $0.00060 | $0.00480 |
| Sonnet 5.5 | $0.00030 | $0.00240 |
| Haiku 4.5 | $0.00015 | $0.00120 |
Grade A, and why
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 1mo 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Variables
Arguments: $ARGUMENTS
Purpose
Some decisions are neither an agent-lookupable fact nor the user's to make. A different person holds the knowledge the decision needs. This skill turns that decision into a questionnaire: a Markdown document the user hands to one person to fill in async, or fills out together in a meeting. The recipient holds knowledge the user lacks; the questionnaire pulls it out of them.
This is the third routing bucket beside /planning:interview's facts-vs-decisions split: facts the agent looks up, decisions the user makes, and person-held decisions this skill hands off. The deliverable is the document, full stop. Delivery happens out-of-band (email, chat, a meeting), never through this skill.
Stance
Interview 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. Never quiz the user about the subject the recipient holds. That knowledge gap is exactly why the questionnaire exists. The questions in the document target the gap between what the recipient knows and what the user needs.
Route away when no one else holds the answer. If it emerges that the user can answer the decision themselves (no third-party knowledge holder), do not produce a questionnaire for nobody. Invoke /planning:interview via the Skill tool and stop this skill. Never invent a recipient to justify the artifact. The hand-off is a real Skill invocation, not a mention: the model can land here from a natural-language request, and bare /name prose would read as advice to the human and strand the decision unresolved.
The loop
-
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.
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.
- 1mo ago Changed 9652b005b682
- 1mo ago First seen · 46 lines · 151 tokens per session scan A 91b8fc6df6ef
questionnaire is a skill published in the GitHub repository melodic-software/claude-code-plugins (26 stars, last pushed today), licensed MIT. It adds 151 tokens to every session and 1,201 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 2026-09-03.
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