hermes-agent VS agentskills

NousResearch publishes a detailed security policy for Hermes Agent. It is unusually clear about what the project treats as load-bearing and what it does not. If you operate Hermes in the cloud, read it first; this post is the operator-friendly companion, not a replacement.

OpenCode seemed perfectly workable as a programming assistant. As personal assistants, they all fall short. It's too difficult to really shape their output.

I was briefly impressed with OpenClaw a few times, but ultimately was turned off by not being able to get the models to stop being so damnably verbose. I thought I made progress for a while by having it tweak its soul, iterate, switch models, iterate, switch models, fuse the results, iterate... but ultimately it's all forgotten early in each session. And then one day it killed itself by rebuilding the container it was inside.

Hermes apparently has some plagiarism issues they're trying to cover up [0] and I was deeply unimpressed with their janky, flickery CLI that force-enables a bulky obnoxious header.

Nanoclaw and nanobot seemed fine, but not notably different. There were some common bugs and glitches that caused some minor data loss while configuring nanobot. After that I just deciding to start hacking my own together.

What I really want in a harness is being able to actually control and rewrite the entire context window, like Zed's Text Threads before they obnoxiously and inexplicably removed what, to me, was their most powerful and distinguishing feature.

[0] https://github.com/NousResearch/hermes-agent/issues/10232

I've opened a design discussion to argue this out before anyone writes a line of it, because a surprise PR to a security-sensitive module is the wrong way to start. Feedback from people who've thought about supply-chain trust is what I appreciate.

Source: https://github.com/NousResearch/hermes-agent

Hermes Agent is an open-source AI agent framework.

I opened my Hermes Agent logs and found something I did not know existed: an auxiliary: block with twelve background tasks. Compression, web extraction, vision, session search, skills matching — all running silently every time I typed a message. Every task was set to provider: auto. And because I had no API keys for the fallback chain, every one silently fell back to kimi-k2.6, my one-trillion-parameter main model.

The adjacent tools worth tracking: the OpenAI Chrome plugin, BrowserCode, Autobrowse, browser-harness, Pi browser extensions, and Hermes browser skills. [13] [14] [6] [15] [16] [12] The category is logged-in browser work: support queues, internal tools, research, scraping, QA, admin ops, and anything where the useful data sits behind a session.

hermes-telemetry solves both by giving you real-time observability and automatic budget enforcement for Hermes Agent.

If you haven't encountered it yet: Hermes Agent is an open-source agentic system from Nous Research. The key differentiators that caught my attention:

tl;dr - yes, the current term de jour is "context engineering", which ironically involves "skill" writing among other things. There is definitely a way to prompt/message them, such as asking questions or not cursing at them.

Read Antropic blog/docs, they have among the best knowledge share. These too:

https://agents.md

https://agentskills.io/

While tools handle actions, skills handle behavior. A skill is a reusable set of instructions or a workflow that tells an agent how to perform a specific type of task well.

There is also an Agent Skills bundle in the repo that teaches your coding agent to use the CLI properly. Drop it into your OpenCode, OpenClaw, or Claude Code skills directory and the agent learns to prefer --json, branch on exit codes, and read status --json before configuring an OpenAI-compatible client.

Here's the detail that turns a nice feature into an industry shift. The skill format isn't a Hermes invention or a Nous Research lock-in. It was originally developed by Anthropic, released as an open standard, and it has since been adopted by a genuinely surprising list of tools: Claude Code, Cursor, OpenAI's Codex, Gemini CLI, GitHub Copilot, Goose, OpenHands, Letta, and a few dozen more.

It's built as an Agent Skill, so you install it as a skill and don't touch the agent itself. It turned out better than I expected, so I open-sourced it.

Skills, the procedural memory layer. Anthropic's skills documentation covers the platform-native version. The open standard at agentskills.io makes skills portable across agents — Claude Code, Codex, Gemini CLI, the Hermes orchestrator from yesterday's piece. A skill captures a pattern you have already executed enough times to formalize.

@shopify/ucp-cli is agent-first by design. Every command takes and returns structured JSON, so a model can compose payloads and parse results without scraping human-readable output. ucp skills add installs a bundled SKILL.md — the operating manual that teaches an agent when to search versus discover, how to render totals, how to surface required disclosures, and when to hand off to a human. The full command surface mirrors the UCP shopping service:

Anthropic formalized the skills concept for Claude Code, and the format is now an open standard gaining adoption across AI coding tools. The refund scenario above could be expressed as in the following example:

An open standard. The format is published at agentskills.io, so the same SKILL.md file works across more than just Claude. Community marketplaces collectively list more than 4,000 skills as of May 2026. The official source remains github.com/anthropics/skills.

They're folders of instructions, scripts, and reference materials that Claude loads on demand. They live in .claude/skills/ and follow the Agent Skills open standard, which means they work across Claude Code, Codex, Gemini CLI, Cursor, and others.