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This is a submission for the Hermes Agent Challenge : Write About Hermes Agent I Didn't Start With Hermes Six months ago I started building a set of agent skills and personas for how I build software. Not generic prompts — opinionated role files. A /backend-architect that owns schema and recommendation logic. A /test-engineer that writes Vitest coverage and flags weak acceptance criteria. A /project-manager that maintains planning docs and closes iterations cleanly. These roles have evolved across multiple projects. They have layering rules, discovery checklists, inheritance from a base engineering discipline file. They produce consistent, reviewable work because they're scoped — the backend architect doesn't touch test files, the test engineer doesn't redesign the schema, each persona has a defined mandate and exits cleanly. When I heard about Hermes Agent, my first instinct wasn't "let me learn a new system." It was: can I run my existing system inside this? The answer is yes. That's what this article is about — what it looks like to bring a mature workflow into Hermes, what you gain, where it breaks down, and what I'd do differently. What Hermes Is (and Isn't) to Someone Who Already Has a Workflow Hermes is an LLM-agnostic orchestration layer. It has its own skill system, its own soul.md concept for persistent agent identity, built-in cron scheduling and MCP management. All of that is real and useful. But it's also a runtime. If you have skills that work, you can bring them in. I installed a local Hermes instance — few clicks, straightforward setup — and ran it inside VSCode's integrated terminal pointed at my existing persona files. No migration. No rewrite. My /backend-architect runs in Hermes the same way it runs in Claude Code. Before settling on this, I'd tried a couple of other paths — a VPS instance with a Telegram interface for ideation, and attempting to build through a browser-based terminal. The VPS was fine for sketching ideas. The browser terminal ma

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent What I Built Real brewing knowledge lives in human experience — in roaster guides, in community notes, in what a barista learned from last Tuesday's pour. It doesn't accumulate anywhere. Every brew is forgotten. Ask any AI and you get statistical averages: 93°C, 1:16 ratio, four minutes. Technically defensible. Practically generic. Worse still for rare origins where training data is thin. Demo For coffee drinkers Visit brew-guide-production.up.railway.app . No account. No setup. No AI client required. Pick your coffee origin, roast level, and brew method. What comes back isn't a generic recipe — it's community consensus: the grind, temperature, ratio, and brew time that real people have logged and rated for that origin, plus step-by-step technique guidance (bloom timing, pour stages, agitation style). If data is sparse for your origin, the confidence tier says so honestly and falls back to method defaults rather than making something up. This is for the person who just picked up a bag of Kenyan peaberry and wants to know how to do it justice. It works for anyone who cares about their cup — no technical knowledge required. For developers and AI clients Connect to any MCP-capable client in one line: https://brew-guide-production.up.railway.app/mcp Ask your AI: "recommend a pour over for Ethiopian light roast." What comes back is a traceable community consensus object: brew parameters, a confidence tier (high/medium/low), the source brews that contributed, and method-specific technique guidance. You can see where the knowledge came from and how certain the system is — a fundamentally different epistemic object from an AI-generated recipe. Code GitHub: yuens1002/brew-guide Five MCP tools — get_brewing_methods , recommend , log_brew , search_brews , compare_brew — over Streamable HTTP transport. Public, no auth required. My Tech Stack Layer Technology HTTP Hono 4 + @hono/node-server MCP @modelc

How long did it take? How many ideas did you go through? What made you stick to an idea vs pivot?

Built an open JSON Schema for defining AI agent teams. Multi-agent systems are becoming a real deployment pattern — not single assistants, but teams with roles, handoffs, and human checkpoints. But there's no shared way to define one that travels across frameworks. Every implementation is scattered, locked to whichever tool you picked first. Built the schema to fix that. The schema lives at schema.openenvelope.org and is registered in SchemaStore, so if you drop a .envelope.json file in VS Code