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 cyborg-garden/hermes-agent-mt --skill decision-questionnairegit clone --depth 1 https://github.com/cyborg-garden/hermes-agent-mtWrote 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/cyborg-garden/hermes-agent-mt/decision-questionnaire)<a href="https://agentmods.dev/skills/cyborg-garden/hermes-agent-mt/decision-questionnaire"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/decision-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/cyborg-garden/hermes-agent-mt/decision-questionnaire"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/decision-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 | $0.00015 | $0.00966 |
| Opus 5.5 | $0.00006 | $0.00386 |
| Sonnet 5.5 | $0.00003 | $0.00193 |
| Haiku 4.5 | $0.00002 | $0.00097 |
Grade A, and why
decision-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 yesterday.
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
100% identical to decision-questionnaire — 0 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Questionnaire
Turns 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 in a meeting. The recipient holds knowledge the user lacks; the questionnaire pulls it out of them.
Ported from mattpocock/skills' MIT-licensed to-questionnaire skill.
When to Use
- A decision blocks on facts or judgment held by someone else (a domain expert, a stakeholder, a vendor contact, ops)
- The user says "I need to ask X about this" or keeps deferring a decision pending someone else's input
- Preparing for a meeting where specific answers must come back
Do NOT use when the answer is discoverable from the environment (codebase, docs, web) — find it yourself first.
Core Principle: Interview the Send, Not the Subject
The user cannot answer the subject-matter questions (that's the point), but they can ALWAYS answer questions about the send. Interview them only about that, in two short exchanges:
- Who is it going to? Role, expertise, 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? The specific decisions or facts the user can't resolve alone. Done when you have a concrete list of what the user must walk away able to do or decide.
Then write the questionnaire: draft questions aimed at the gap between
what the recipient knows and what the user needs, following the structure
below. Write it to decision-questionnaire-<slug>.md in the current
directory (slug from the topic) and report the absolute path. Done when the
file exists and every item from step 2 is covered by a question.
Document Structure
Frame it 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). Group under ## headings by theme once there are
more than a handful.
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.
- yesterday First seen · 116 lines · 15 tokens per session scan A 84a781708b86
decision-questionnaire is a skill published in the GitHub repository cyborg-garden/hermes-agent-mt (14 stars, last pushed yesterday), licensed MIT. It adds 15 tokens to every session and 966 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to decision-questionnaire, differing in 0 lines, and is treated as a copy.
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mnemosyne
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mnemosyne-memory-override
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auth-guard
An HTTP access-control add-on for protecting a VRChat assistant service exposed on a local network, the public internet, Docker, or behind a reverse proxy. It accepts Bearer tokens, API keys, or URL tokens.