Use for reverse engineering stripped Go and Rust binaries including runtime recognition, pclntab/moduel data recovery, panic strings, and idiomatic decompilation recovery.
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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。
□ 识别 go.buildid、runtime 符号残留、pclntab
□ GoReSym / redress / IDA Go 插件恢复函数名
□ 注意 interface、slice、string 结构在反编译中的形态
□ 网络/加密库路径：crypto/* net/http
□ panic 字符串、rust_begin_unwind、crate 路径暗示
□ 范型实例化导致的代码膨胀；先定位字符串 xref
□ 异步/tokio 状态机需结合交叉引用
□ 仍可用 Frida；注意 Go 栈与调度
□ 优先日志与配置字符串驱动断点
| 工具 | 用途 |
|---|---|
| GoReSym | Go 元数据 |
| IDA/Ghidra + Go/Rust 插件 | 反编译 |
| radare2 | 快速字符串 |
| strings / rabin2 | 分诊 |
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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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