by 0rangec3t · AI Tool · ★ 328
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 328 |
| Forks | 42 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 59/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-03 |
| Created | 2026-07-30 |
| Platforms | claude-code, python |
| Est. Tokens | ~16k |
Black cat 一只头戴女巫帽的魔法黑猫。 Claude code Redteam skill 做这个 Skill 的原因 最近一直在看自动化渗透相关，尝试性写了几个框架，但是最后效果都不太满意。相反，之前当玩具写的 Skill 放在 Claude Code 里面，效果却出乎意料地好。 本 Skill 主要使用假设—证据驱动，为了模仿人在真实场景下的渗透测试。 与市面上所有 pentest skill 的本质区别 市面上的 pentest skill 都是 Pipeline（流水线）：Recon → Scan → Exploit → Report。每个 phase 绑定固定工具，失败就跳到下一步，没有回边。 我们的设计是 State Machine（状态机）：RECON ⇄ ENUMERATE ⇄...
These tools work well together with Black-cat for enhanced workflows:
Looking for a Black-cat alternative? If you're comparing Black-cat with other ai tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Watchtower is a simple AI-powered penetration testing automation CLI tool that leverages LLMs and LangGraph to
AI Powered penetration testing Platform for offensive security research
证据驱动的 AI 资产测绘与安全研判工具。以自然语言串联资产发现、AI 反思、授权验证与报告交付，将 FOFA、Nuclei 与 AI 助手融入同一工作流。原生支持 CLI / MCP / Skill / REST AP
Open-source Claude Code skills, agents, and slash commands for AI-powered penetration testing, bug bounty hunt
Offensive security toolkit for Claude Code
MCP-native security automation workbench (SDK + CLI + MCP) — authorized testing + static AI/MCP attack-surface
Explore other popular ai tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: