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 NousResearch · Codex Skill · ★ 5.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is hermes-agent-self-evolution safe to install? View the security audit →
🧬 Hermes Agent Self-Evolution Evolutionary self-improvement for Hermes Agent. Hermes Agent Self-Evolution uses DSPy + GEPA (Genetic-Pareto Prompt Evolution) to automatically evolve and optimize Hermes Agent's skills, tool descriptions, system prompts, and code — producing measurably better versions through reflective evolutionary search. No GPU training required. Everything operates via API calls — mutating text, evaluating results, and selecting the best variants. $2-10 per optimization run. How It Works GEPA reads execution traces to understand why things fail (not just that they failed), then proposes targeted improvements. ICLR 2026 Oral, MIT licensed. Quick Start bash Install git clone https://github.com/
| Stars | 5,380 |
| Forks | 637 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 65.3343351961741/100 |
| Open Issues | 123 |
| Last Updated | 2026-06-17 |
| Created | 2026-03-09 |
| Platforms | python |
| Est. Tokens | ~3k |
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hermes-agent-self-evolution is ⚒ Evolutionary self-improvement for Hermes Agent — optimize skills, prompts, and code using DSPy + GEPA. It is categorized as a Codex Skill with 5.4k GitHub stars.
hermes-agent-self-evolution is primarily written in Python.
You can find installation instructions and usage details in the hermes-agent-self-evolution GitHub repository at github.com/NousResearch/hermes-agent-self-evolution. The project has 5.4k stars and 637 forks, indicating an active community.
The top alternatives to hermes-agent-self-evolution on Agent Skills Hub include superset, refly, Claude-to-IM-skill. 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: