No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by aliaihub · MCP Server · ★ 212
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-hermes-usecases safe to install? View the security audit →
Awesome Hermes Agent Use Cases A community collection of real-world Hermes Agent use cases — each one backed by public evidence. [!NOTE] Evidence rule. Every use case here links to a primary source: an official Hermes doc, a Nous Research companion repo, a GitHub issue with deployment details, or a first-person blog post. If the only evidence is a tweet or "community build" marketing page, it lives in instead — not in the catalog below. Quick start New to Hermes Agent? The fastest path: bash curl -fsSL https://raw.githubusercontent.c
| Stars | 212 |
| Forks | 19 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.0759375031959/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-06 |
| Created | 2026-04-22 |
| Platforms | mcp, python |
| Est. Tokens | ~16k |
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awesome-hermes-usecases is Curated real-world use cases for Hermes Agent — the self-improving AI agent from Nous Research. Backed by primary sources.. It is categorized as a MCP Server with 212 GitHub stars.
awesome-hermes-usecases is primarily written in Python. It covers topics such as agentic-ai, ai-agent, automation.
You can find installation instructions and usage details in the awesome-hermes-usecases GitHub repository at github.com/aliaihub/awesome-hermes-usecases. The project has 212 stars and 19 forks, indicating an active community.
awesome-hermes-usecases is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-hermes-usecases on Agent Skills Hub include awesome-openclaw, meridian, mcp-client-for-ollama. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: