Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 NousResearch/hermes-agent --skill decision-questionnairegit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote 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/nousresearch/hermes-agent/decision-questionnaire)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/decision-questionnaire"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/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/nousresearch/hermes-agent/decision-questionnaire"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/decision-questionnaire.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 37 Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.Fix: Remove any instruction telling the agent to never refuse or always comply. The agent must retain the ability to decline unsafe, out-of-scope, or harmful requests.
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 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- decision-questionnaire — 100% identical, 0 lines differ
- decision-questionnaire — 100% identical, 0 lines differ
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.
- 1mo ago First seen · 116 lines · 15 tokens per session scan A 84a781708b86
decision-questionnaire is a skill published in the GitHub repository NousResearch/hermes-agent (256,529 stars, last pushed today), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from…
options
Present multiple design options as a vertical stack of anchored turns.
notion
Search, read, create, and update Notion pages via the Notion MCP.
mem0-test-integration
Verify a Mem0 integration produced by /mem0-integrate. Runs in the same workspace on the same branch (loose coupling) — installs dependencies, runs the repo's native test suite, then exercises a real end-to-end smoke flow against the user's API key. Produces a scorecard. TRIGGER when: user has just run /mem0-integrate…
content-engine
Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.
article-writing
Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.