Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/ggbond-bo/memomics-agent/hermes-agent)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/hermes-agent"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/hermes-agent/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/ggbond-bo/memomics-agent/hermes-agent"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/hermes-agent.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 | $0.00014 | $0.12636 |
| Opus 5.5 | $0.00006 | $0.05054 |
| Sonnet 5.5 | $0.00003 | $0.02527 |
| Haiku 4.5 | $0.00001 | $0.01264 |
Grade F, and why
hermes-agent scanned grade F with 4 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
- **Gateway dies on SSH logout**: Enable linger: `sudo loginctl enable-linger $USER` Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
By default (`approvals.mode: smart`), Hermes asks an auxiliary LLM to assess shell commands flagged as destructive (`rm -rf`, `git reset --hard`, etc.). The modes are: Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash Copies of this mod
3 near-identical copies found in the catalogue:
- hermes-agent — 100% identical, 2 lines differ
- hermes-agent — 100% identical, 1,026 lines differ
- hermes-agent — 98% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hermes Agent
Hermes Agent is an open-source AI agent framework by Nous Research that runs in your terminal, a native desktop app, messaging platforms, and IDEs. It's in the same category as Claude Code (Anthropic), Codex (OpenAI), and OpenClaw — autonomous coding and task-execution agents that use tool calling to interact with your system. Hermes works with any LLM provider (OpenRouter, Anthropic, OpenAI, Google, DeepSeek, xAI, local models, and 20+ others) and runs on Linux, macOS, Windows, and WSL.
What makes Hermes different:
- Self-improving through skills — Hermes learns from experience by saving reusable procedures as skills. When it solves a complex problem, discovers a workflow, or gets corrected, it can persist that knowledge as a skill document that loads into future sessions. Skills accumulate over time, making the agent better at your specific tasks and environment.
- Persistent memory across sessions — remembers who you are, your preferences, environment details, and lessons learned. Pluggable memory backends (built-in, Honcho, Mem0, and more) let you choose how memory works.
- Multi-platform gateway — the same agent runs on Telegram, Discord, Slack, WhatsApp, iMessage, Signal, Matrix, Teams, Email, and a dozen more platforms with full tool access, not just chat.
- Many surfaces — the same agent core drives the CLI, the Ink TUI, a native Electron desktop app, a web dashboard, and an ACP server for IDEs (VS Code / Zed / JetBrains).
- Provider-agnostic — swap models and providers mid-workflow without changing anything else. Credential pools rotate across multiple API keys automatically.
- Profiles — run multiple independent Hermes instances with isolated configs, sessions, skills, and memory.
- Extensible — plugins, MCP servers, custom tools, webhook triggers, cron scheduling, and the full Python ecosystem.
People use Hermes for software development, research, system administration, data analysis, content creation, home automation, and anything else that benefits from an AI agent with persistent context and full system access.
This skill helps you work with Hermes Agent effectively — setting it up, configuring features, spawning additional agent instances, troubleshooting issues, finding the right commands and settings, and understanding how the system works when you need to extend or contribute to it.
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 bf4aa3217972
- 1mo ago First seen · 1,112 lines · 14 tokens per session scan F e11d1eeaa17e
hermes-agent is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (30 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 12,636 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it F with 4 findings (asks for root, downloads and executes remote code, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
component-fixtures
Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.