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 lingbol088-spec · Codex Skill · ★ 874
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is reverse-flow-skill safe to install? View the security audit →
reverse-flow-skill 面向 AI Agent / Codex 的本地 CTF 逆向工程流程技能。加载后通过 “真心为你” 进入逆向模式，默认在本地沙盒、CTF、crackme、wargame 或训练靶场环境中工作，按 分析 → 报告 → 逆向 → 深度逆向 → 漏洞研判 → 用户选择下一步 的流程推进。 项目简介 是一个给 AI Agent 使用的逆向工程技能。它把逆向任务拆成稳定流程，让智能体在本地沙盒中完成样本分诊、工具链检查、静态分析、深度逆向、漏洞研判和报告输出。 项目采用 英文内核提示词 + 中文用户交互： 内部执行规则、工具选择和流程控制使用英文，提升模型稳定性。 启动词、报告结构和下一步菜单默认使用中文。 用户表达不清时，技能会先做意图恢复，而不是直接停止。 启动词 启动后提示： 默认上下文 该技能默认用户处于： 本地 CTF 环境 crackme / wargame 本地训练靶场 授权沙盒逆向实验 离线样本分析环境 用户不需要每轮重复说明“这是 CTF”或“这是本地靶场”。 口语化需求归一化 工作流 项目结构 安装 复制技能目录到 Codex 技能目录： 使用示例 脚本示例 powershell python ".\skills\reverse-flow\s
| Stars | 874 |
| Forks | 324 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 61.6585690683751/100 |
| Open Issues | 5 |
| Last Updated | 2026-07-24 |
| Created | 2026-06-30 |
| Platforms | codex, python |
| Est. Tokens | ~4k |
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reverse-flow-skill is 面向 AI Agent / Codex 的本地 CTF 逆向工程流程技能。加载后通过“真心为你”进入逆向模式，默认在本地沙盒、CTF、crackme、wargame 或训练靶场环境中工作，按“分析 → 报告 → 逆向 → 深度逆向 → 漏洞研判 → 用户选择下一步”的流程推进。. It is categorized as a Codex Skill with 874 GitHub stars.
reverse-flow-skill is primarily written in Python.
You can find installation instructions and usage details in the reverse-flow-skill GitHub repository at github.com/lingbol088-spec/reverse-flow-skill. The project has 874 stars and 324 forks, indicating an active community.
reverse-flow-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to reverse-flow-skill on Agent Skills Hub include Claude-to-IM-skill, skillshare, agent-skill-creator. 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: