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 liyue-aigc · Codex Skill · ★ 1.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is female-portrait-director safe to install? View the security audit →
English 한국어 Female Portrait Prompt Director Skill Female Portrait Prompt Director Skill is a structured prompt-generation and visual-direction system for AI image creation. V1.4 routes each request through a single on-demand style file, locks explicit parameters, expands visual modules, and fuses them into a coherent photographed moment. This project is not a generic prompt collection. It is an extensible female portrait prompt Skill framework. Project Scope Generate complete prompts from a small set of input parameters. Preserve the user's explicit requirements while visually expanding facial features, body shape, outfit, scene, camera and pose, lighting, filters, platform purpose, and negative constraints. Subjects must be clearly adult women. Outputs emphasize realistic photography, restrained expression, visual coherence, and stable generation.
| Stars | 1,333 |
| Forks | 167 |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.3771749615697/100 |
| Last Updated | 2026-07-15 |
| Created | 2026-05-30 |
| Platforms | codex |
| Est. Tokens | ~17k |
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female-portrait-director is A modular Codex Skill for directing and expanding detailed AI female portrait prompts.. It is categorized as a Codex Skill with 1.3k GitHub stars.
You can find installation instructions and usage details in the female-portrait-director GitHub repository at github.com/liyue-aigc/female-portrait-director. The project has 1.3k stars and 167 forks, indicating an active community.
female-portrait-director is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to female-portrait-director on Agent Skills Hub include godogen, MeiGen-AI-Design-MCP, quint-code. 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: