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 asimons81 · Codex Skill · ★ 125
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is hermes-field-kit safe to install? View the security audit →
Hermes Field Kit Field-tested, open-source skills for Hermes Agent. Tags: · · · · · · · Hermes Field Kit is a curated collection of reusable workflows that have earned their place through real, repeated use. It is intentionally not a bulk skill dump. Experimental entries are explicitly marked while they accumulate the field evidence required for stable promotion. Current status Version 1.0.1 is the current tagged corrective release. The working catalog contains thirteen stable skills plus experimental 0.1.0 and 0.1.0. The catalog includes private-by-default analytics, source-locked writing, and an operational Field Kit organized around: The repository applies the same admission rule to every future s
| Stars | 125 |
| Forks | 10 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 71.560988344073/100 |
| Last Updated | 2026-09-09 |
| Created | 2026-07-20 |
| Platforms | python |
| Est. Tokens | ~15k |
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hermes-field-kit is Field-tested, open-source skills for Hermes Agent.. It is categorized as a Codex Skill with 125 GitHub stars.
hermes-field-kit is primarily written in Python. It covers topics such as agent-skills, ai-agents, hermes-agent.
You can find installation instructions and usage details in the hermes-field-kit GitHub repository at github.com/asimons81/hermes-field-kit. The project has 125 stars and 10 forks, indicating an active community.
hermes-field-kit is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to hermes-field-kit on Agent Skills Hub include maestro, claudepro-directory, open-skills. 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: