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 swping999 · Codex Skill · ★ 81
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is scene-card-studio safe to install? View the security audit →
Scene Card Studio AI visual director for turning personal photos into structured, editable visual narratives. 中文 · English ▶ Play the 6-second opening film This is not a style-transfer repository. It is a Scene Card–based visual narrative engine for personal photography. Scene Card Studio turns observable photo evidence into editable narrative decisions, versioned generation prompts, directed images, and deterministic layouts. Ins
| Stars | 81 |
| Forks | 9 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 61.6990414082804/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-05 |
| Created | 2026-08-09 |
| Platforms | codex, python |
| Est. Tokens | ~18k |
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scene-card-studio is AI visual director for turning personal photos into structured, editable visual narratives.. It is categorized as a Codex Skill with 81 GitHub stars.
scene-card-studio is primarily written in Python. It covers topics such as ai-tools, codex-skill, generative-ai.
You can find installation instructions and usage details in the scene-card-studio GitHub repository at github.com/swping999/scene-card-studio. The project has 81 stars and 9 forks, indicating an active community.
scene-card-studio is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to scene-card-studio on Agent Skills Hub include mcp-image, ARIS-Movie-Director, comfy-pilot. 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: