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 shiro-0x · MCP Server · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is hersona safe to install? View the security audit →
hersona English · 日本語 Build once. Keep personality everywhere. Composable personalities for every LLM. 346 reusable character attributes for AI agent personas — compose a persona from personality / speech / archetype / visual / hobby templates, measure that it actually holds up in conversation, and port it to any LLM or agent framework. MIT (code) + CC0 (templates). CLI, MCP server, and Hermes Agent skill. Docs · PyPI · Full reference Quick start (30 seconds) bash pip install hersona # Python = 3.11 hersona blend personality/tsundere speech/keigo --weight strong # injection block → stdout hersona export personality/tsundere speech/keigo -
| Stars | 51 |
| Forks | 1 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 52.0310638192928/100 |
| Last Updated | 2026-09-17 |
| Created | 2026-06-04 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~13k |
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hersona is 346 reusable character attributes for AI agent personas — compose, measure, and port system-prompt personas. Build once. Keep personality everywhere.. It is categorized as a MCP Server with 51 GitHub stars.
hersona is primarily written in Python. It covers topics such as ai-agents, aituber, anime-character.
You can find installation instructions and usage details in the hersona GitHub repository at github.com/shiro-0x/hersona. The project has 51 stars and 1 forks, indicating an active community.
hersona is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to hersona on Agent Skills Hub include google-analytics-mcp, youtube-connector-mcp, flowlens-mcp-server. 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: