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 glassity/agent-skills --skill questionnaire-rungit clone --depth 1 https://github.com/glassity/agent-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/glassity/agent-skills/questionnaire-run)<a href="https://agentmods.dev/skills/glassity/agent-skills/questionnaire-run"><img src="https://agentmods.dev/badge/skills/glassity/agent-skills/questionnaire-run/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/glassity/agent-skills/questionnaire-run"><img src="https://agentmods.dev/badge/skills/glassity/agent-skills/questionnaire-run.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 | $0.00091 | $0.02366 |
| Opus 5 | $0.00046 | $0.01183 |
| Sonnet 5 | $0.00018 | $0.00473 |
| Haiku 4.5 | $0.00009 | $0.00237 |
Grade A, and why
questionnaire-run 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 9d 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run a questionnaire — mutations, two-phase, driven by AskUserQuestion
When to use
- "Complete the service onboarding questionnaire for service
svc-…". - "Fill in the questionnaire for opp X" — resolve the related service first with
opp-showoropp-listif it is not obvious. - "Finish onboarding for service S" — the questionnaire is the gate.
- "Complete the
<name>questionnaire for<entity>" — the generic case.
Do not use this skill for ad-hoc interviews. It exists to drive a
server-defined questionnaire whose schema comes back from
glassity questionnaire show <id>.
Binary check
Run command -v glassity before the first CLI call. If it is missing, stop and
tell the user to install it with brew install glassity/tap/glassity or from
https://github.com/glassity/cli/releases. No account yet? Sign up at
app.glassity.cloud.
The rule that makes this safe
glassity questionnaire submit is a server mutation behind the binary's
two-phase confirmation. Phase 1 makes no network call: it renders the exact POST
it would send, including every collected answer, and issues an HMAC-bound
confirm token. Phase 2 redeems the token and sends those same bytes.
After running phase 1 you must:
- Show the user the full plan from stderr, verbatim. Do not summarise it and do not drop answers — the point is that they see what is being recorded under their name.
- Ask explicitly: "Approve this mutation? (yes/no)"
- Only run phase 2, with
--confirm-token=<token>, after an affirmative reply in the same turn.
Do not run both phases in one bash invocation. Do not add
glassity questionnaire submit to an always-allow rule.
Step 1 — resolve the questionnaire id
In order of preference:
-
The user named it (for example
quest_abc123). Use it; go to step 2. -
An upstream record carries it. Assignment and service detail both carry a
questionnairesarray of{id, definition, completed_at}. Read it with the raw-request fallback, which reaches endpoints the typed commands do not wrap:
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.
- 9d ago First seen · 215 lines · 91 tokens per session scan A 1f91773f817c
questionnaire-run is a skill published in the GitHub repository glassity/agent-skills (2 stars, last pushed 27d ago), licensed Apache-2.0. It adds 91 tokens to every session and 2,366 once invoked, about $0.0005 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 2026-08-31.
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