Use for blue-team threat hunting, detection engineering with Sigma/YARA, SIEM query design, and incident detection validation.
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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。
malware-analysis/：样本侧 IOC → 本 skill 落地检测digital-forensics/：案件伪影 → 横向狩猎例：攻击者用 living-off-the-land 做横向
→ 数据源：Sysmon 1/3/10、Windows Security 4624/4648
→ 成功标准：发现异常父进程或罕见账户日志源
□ 基线：正常管理员行为时段与主机
□ 异常：新服务、编码 PowerShell、异常出站
□ 关联：同账号多主机短时登录
# Sigma 骨架见 malware-analysis；本 skill 强调：
# - 误报面
# - 数据源字段映射
# - 响应 playbook 链接
□ 原子测试（Atomic Red Team）仅在授权实验室
□ 回放历史日志验证召回
| 工具 | 用途 |
|---|---|
| Sigma CLI / sigmac | 规则转换 |
| YARA | 文件/内存 |
| SIEM（ELK/Splunk 等） | 查询 |
| osquery | 端点狩猎 |
| Atomic Red Team | 检测验证（实验室） |
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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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