Use for authorized digital forensics including memory dumps, disk timelines, PCAP investigation, artifact triage, and IR evidence preservation.
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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。
protocol-reverse/）malware-analysis/ / threat-hunting/）□ 计算 SHA256；记录时区与采集命令
□ 工作在副本上；原始只读
□ chain of custody 备注写入 timeline
vol -f mem.dmp windows.info
vol -f mem.dmp windows.pslist
vol -f mem.dmp windows.netscan
vol -f mem.dmp windows.cmdline
□ 事件日志：Security / PowerShell / Sysmon
□ 持久化：Run 键、服务、计划任务、WMI
□ 执行痕迹：Amcache、Prefetch、BAM
□ tshark 统计会话与 DNS
□ 导出可疑流 → protocol-reverse 或 malware C2 分析
| 工具 | 用途 |
|---|---|
| Volatility 3 | 内存 |
| Timeline Explorer / Plaso | 超级时间线 |
| tshark | PCAP |
| Eric Zimmerman 工具集 | Windows 伪影 |
| Autopsy / FTK Imager | 磁盘 |
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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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