Use for authorized Active Directory and Windows identity attacks including Kerberos, AD CS, BloodHound paths, NTLM relay, and domain privilege escalation research.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
attack-chain/，本 skill 为 AD 专科# 示例 Impacket / 内置（需凭据与授权）
nxc smb <range> -u user -p pass
bloodhound-python -d domain.local -u user -p pass -c All -ns <DC>
□ Kerberoast / AS-REP → 离线破解
□ ACL 滥用（GenericAll/WriteDacl）
□ 委派（非约束/约束/基于资源）
□ AD CS 模板错误 → Certipy
□ 中继：LLMNR/NBT-NS + ntlmrelayx（确认授权）
□ secretsdump / lsassy / mimikatz（严格授权与清理）
□ PtH / PtT / 黄金票仅在授权红队范围
□ 每步写 Evidence；高危等用户确认
| 工具 | 用途 |
|---|---|
| BloodHound / SharpHound | 路径图 |
| Certipy | AD CS |
| Impacket / NetExec | 横向与枚举 |
| Rubeus / Mimikatz | 票据与凭证（授权） |
| Coercer / Responder | 强制认证 / 投毒 |
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Train and optimize AI agents using Microsoft's Agent Lightning framework with reinforcement learning. Use when setting up agent training, instrumenting agents with tracing, configuring LightningStore, implementing reward functions, or optimizing prompts with RL/APO algorithms.
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Post-run self-evaluation system that scores agent output on correctness, clarity, actionability, and conciseness. Use after /team runs, skill executions, or when explicitly asked to evaluate output quality.
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Create AI marketing videos for ads, promos, product launches, and brand content. Models: Veo, Seedance, Wan, FLUX for visuals, Kokoro for voiceover. Types: product demos, testimonials, explainers, social ads, brand videos. Use for: Facebook ads, YouTube ads, product launches, brand awareness. Triggers: marketing video, ad video, promo video, commercial, brand video, product video, explainer video, ad creative, video ad, facebook ad video, youtube ad, instagram ad, tiktok ad, promotional video, launch video
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Use when building AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output.
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Your AI research and engineering brain trust. 59 named personas across 8 cells covering frontier labs, applied product, model architecture, reasoning/RL/agents, alignment and interpretability, theory and science of DL, multimodal and…
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Use when designing a new REST or GraphQL API, reviewing an API spec before implementation, setting team API standards, or migrating REST to GraphQL. Covers resources, HTTP semantics, pagination, error handling, and pitfalls.
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