Workflow
Oh-My-Hermes (OMH)
Curated harness and skills pack for Hermes Agent — install once to get optimized configurations, productivity skills, and agent power-ups out of the box.
hermesharnessskills-packconfigurationproductivity
Verified 8 days ago
Skill details
- For
- Hermes Agent
- Author
- rlaope
- Install
- git clone https://github.com/rlaope/oh-my-hermes && follow README
- Dependencies
- Hermes Agent
- Python 3.11+
Oh-My-Hermes (OMH)
What it is
Oh-My-Hermes (OMH) is a curated harness and skills collection for Hermes Agent. Think of it as "oh-my-zsh for Hermes" — install it once and get a set of optimized configurations, productivity-enhancing skills, and agent power-ups that maximize what Hermes can do out of the box.
Who it targets
- New Hermes users who want a strong starting configuration without manual tuning
- Multi-agent operators looking for a shared baseline across team members
- Power users who want a community-maintained skills pack they can extend
What it does
- Pre-configured harness. Optimized Hermes settings for common workflows (coding, research, writing, ops).
- Curated skill bundle. A selection of community skills pre-wired and ready to use.
- One-command install. Clone and run the setup script — no manual skill-by-skill installation.
- Python-based. Easy to inspect, modify, and contribute back.
- Active community. 61+ stars on GitHub with regular updates as of July 2026.
How to install
- Clone the repository:
git clone https://github.com/rlaope/oh-my-hermes - Follow the README for installation instructions.
- Restart Hermes to pick up the new configuration and skills.
- Review which skills are enabled and adjust for your use case.
Why it matters
Hermes Agent is powerful but comes with a blank slate. OMH gives you a opinionated starting point — the equivalent of a senior engineer's dotfiles — so you skip the configuration phase and start producing immediately. It is especially valuable for teams standardizing on a shared Hermes setup.
Limitations
- Opinionated — may conflict with existing custom configurations.
- Skills pack may include capabilities you do not need (review before deploying).
- Community-maintained — verify skill quality before using in production.
- Python dependency management can be tricky across different environments.