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 kadevin · Codex Skill · ★ 709
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ilab-conjure safe to install? View the security audit →
iLab CONJURE 多模型 AI 图片生成工作台 · GPT Image / Gemini · 图库、模板、历史库与并发任务 <img alt="FastAPI WebUI
| Stars | 709 |
| Forks | 114 |
| Language | JavaScript |
| Category | Codex Skill |
| License | AGPL-3.0 |
| Quality Score | 65.2427480431122/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-18 |
| Created | 2026-06-08 |
| Platforms | browser, cli, codex, node |
| Est. Tokens | ~15k |
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ilab-conjure is 面向 GPT-image-2 的 AI 图片生成 WebUI 工作台，支持 Codex Responses 与 OpenAI 兼容 API 接入，内置公用图库、多类型 Chip 快捷引用、提示词模板、多任务并发和本地队列管理。An AI image generation WebUI workbench for GPT-image-2 with Codex Responses and OpenAI-. It is categorized as a Codex Skill with 709 GitHub stars.
ilab-conjure is primarily written in JavaScript. It covers topics such as ai-art, ai-image-generation, cli.
You can find installation instructions and usage details in the ilab-conjure GitHub repository at github.com/kadevin/ilab-conjure. The project has 709 stars and 114 forks, indicating an active community.
ilab-conjure is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to ilab-conjure on Agent Skills Hub include gpt_image_2_skill, os-ai-computer-use, mcp-image. 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: