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 grill-megit 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/grill-me)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/grill-me"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/grill-me/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/grill-me"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/grill-me.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 66 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00012 | $0.01083 |
| Opus 5 | $0.00006 | $0.00541 |
| Sonnet 5 | $0.00002 | $0.00217 |
| Haiku 4.5 | $0.00001 | $0.00108 |
Grade A, and why
grill-me 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 6d 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
1 near-identical copy found in the catalogue:
- grill-me — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grill Me
Stress-tests a plan through structured adversarial questioning before any code is written. Models the plan as a design tree — every decision branches into the decisions that hang off it — and interviews the user in rounds until every branch is resolved and nothing is silently assumed.
Combines the phase discipline of the original with the frontier-rounds
mechanic from mattpocock/skills' grilling.
When to Use
- User says "grill me", "interview my plan", "stress test this idea"
- Before complex work: auth flows, schema changes, migrations, payments
- A plan has unresolved decisions or seems vague
- Before
subagent-driven-developmentdecomposition
Do NOT use for existing code (use requesting-code-review) or simple one-off
tasks.
Prerequisites
None. The skill works on any plan or raw idea.
Core Mechanic: Frontier Rounds
Map the plan as a design tree. The frontier is every decision whose prerequisites are already settled — the questions you can ask NOW without guessing at answers you haven't heard yet.
Work in rounds: ask the whole current frontier in one message, numbered, each question carrying your recommended answer. Then wait. A question whose answer depends on another question still open in this round belongs to a LATER round, not this one.
Format each round like so:
❓ Q1 — <question title>: <question body, options if relevant>
➡️ Recommendation: <your recommended answer + one-line why>
❓ Q2 — <question title>: <question body>
➡️ Recommendation: <...>
Each answer reshapes the tree: settled decisions push the frontier outward and unblock dependent questions. Recompute the frontier and ask the next round.
Facts are your job; decisions are the user's. When a frontier question
needs a fact from the environment (codebase, filesystem, config, docs), find
it yourself with search_files / read_file / terminal — or dispatch a
subagent via delegate_task for a heavy exploration. Never ask the user for
anything you could look up. Don't block on an exploration: only the questions
downstream of it wait; ask the rest of the frontier now.
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.
- 6d ago First seen · 118 lines · 12 tokens per session scan A 6e3b3b81c1ad
grill-me is a skill published in the GitHub repository NousResearch/hermes-agent (243,598 stars, last pushed today), licensed MIT. It adds 12 tokens to every session and 1,083 once invoked, about $0.0001 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 2026-09-03.
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claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude…
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Present multiple design options as a vertical stack of anchored turns.
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…
9router-web-search
Web and X search via 9Router /v1/search using Tavily / Exa / Brave / Serper / SearXNG / Google PSE / Linkup / SearchAPI / You.com / Perplexity / Xquik. Use when the user wants to search the web, find articles, or search public X posts.
safe-refactor
Restructure code while preserving behavior. Use for extraction, consolidation, ownership moves, or cleanup where verification must bracket structural edits.