This is a submission for the Hermes Agent Challenge
What I Built
I built hermes-skill-hq, a skill knowledge base for Hermes Agent.
The problem it solves is simple: once an agent has access to hundreds or thousands of skills, the hard part is no longer "can the agent do this?" It becomes "does the agent know the right skill exists, and will it load it before improvising?"
Hermes Skill HQ gives the agent a structured operating model for that:
- Navigator — map a task to the right skill quickly.
- Advisor — proactively notice when a skill would help.
- Authoring companion — identify gaps and help create new skills when no good one exists.
Instead of treating skills as a passive folder of files, HQ turns them into an active knowledge base. It tells the agent where to look first, when to stop searching, how to avoid wasting context, and how to maintain the index over time.
In practice, it helps Hermes answer questions like:
- "Is there already a skill for this task?"
- "Should I load a specific workflow before touching files?"
- "Is this a missing skill we should author?"
- "How do I keep the skill catalog from becoming stale?"
Demo
A typical interaction looks like this:
User: Help me set up a ComfyUI workflow.
Hermes: Checked HQ's INDEX — there is a ComfyUI skill for image, video, and audio workflows. Want me to load it before we continue?
Or, when no skill exists:
User: I want to automate a workflow for a tool HQ has never seen.
Hermes: INDEX has no match. Hub search has no match. That looks like a gap. Want me to create a new skill and validate it with skill-creator?
The important part is not the wording; it is the behavior. Hermes stops guessing. It checks the knowledge base first, explains what it found, and either loads the right skill or identifies a skill gap.
Code
Repository:
CodeSigils
/
hermes-skill-hq
THE definitive skill knowledge base for any Hermes AI agent. Curated task→skill INDEX, proactive advisor, marketplace navigation, skill-authoring companion.
hermes-skill-hq
THE skill knowledge base for any Hermes AI agent.
When you start a session, Hermes checks this first. It knows which skills exist, recommends ones you might need before you ask, and helps you create new ones for tasks that don't have one yet.
How It Works For You
You don't manage skills manually — Hermes does it.
At the start of each session, Hermes reads HQ's INDEX and keeps skill opportunities in mind. When it detects you're working on something that has a skill — even one you haven't loaded — it tells you:
"I noticed you're working with ComfyUI images — want me to load
comfyui? It handles image and video generation."
You say yes or no. That's it.
What HQ Does For You
What
How
Skill discovery
Knows ~2043 skills from the Hermes hub + skills.sh ecosystem
Proactive recommendations
Suggests skills based on what you're
The project is designed to live at the standard Hermes knowledge-base path:
git clone https://github.com/CodeSigils/hermes-skill-hq.git ~/.hermes/kb
Then it can be added to Hermes through external_dirs:
external_dirs:
- ~/.hermes/kb
After refreshing Hermes, the knowledge base becomes available as a normal skill named hermes-skill-hq.
My Tech Stack
- Hermes Agent
- Agent Skills /
SKILL.mdformat - Markdown knowledge base files
- A curated task-to-skill index
- Local Hermes hub cache with roughly 2,000 skills
- External skill marketplace references
- GitHub for versioning and distribution
The repo is intentionally lightweight. Most of the value is in the structure and the operating contract, not in a big application framework.
How I Used Hermes Agent
Hermes Agent is both the runtime and the development environment for this project.
I used Hermes to inspect the existing skill ecosystem, compare local skills against hub and marketplace sources, update the task-to-skill index, trim overly large references, and keep the documentation accurate. The agent was especially useful for repetitive knowledge-maintenance work: searching files, checking stale references, editing Markdown, validating instructions, and updating related skill docs when a workflow changed.
The most useful agentic capabilities were:
- Tool use: Hermes could inspect the repo, read source files, search across the knowledge base, and apply targeted edits.
- Persistent skills: Workflows that proved useful could be turned into reusable skills instead of being re-explained every session.
-
Memory and session continuity: Hermes could remember durable conventions, like the canonical install path
~/.hermes/kb/, while avoiding stale task-progress notes. - Skill loading: The project depends on the idea that agents should load specialized instructions before acting. HQ makes that behavior explicit and systematic.
- Self-improvement loop: When Hermes finds a gap, it can help author a new skill, validate it, and add it back into the index.
Hermes Skill HQ is essentially a meta-skill: it helps Hermes decide which other skill to use. That makes the agent more reliable, less ad hoc, and more transparent about why it is choosing a particular workflow.
The design goal is not to make the agent read everything. In fact, a major part of the project is teaching the agent when to stop. HQ uses a layered search strategy: check the index first, search the hub second, and only expand to marketplaces when simpler sources fail. That keeps context usage under control while still giving the agent a path to deeper discovery when needed.
For me, that is the core value: Hermes Skill HQ turns a large pile of reusable capabilities into a navigable, maintainable system.