Home / yuecao365 / offercome · src/lib/mock-interviews/skills/go/SKILL.md · GitHub

go skillA

Name: go
Author: yuecao365

go is agent-read markdown (skill) from yuecao365/offercome: Go 后端怎么面：goroutine 调度、channel、GC 与逃逸、pprof、gRPC。JD 点名 Go 时读。.

Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.

What the file says

## 面试官在意什么

Go 后端在国内主要分布在云原生基础设施、中间件、高并发业务（IM、直播、游戏、广告）与出海公司，日常是用 Gin/Kratos/go-zero 或标准库写服务、用 gRPC 做内部通信、跑在 Kubernetes 上、靠 pprof 与 trace 排查性能。Go 面试有一条清晰主线：goroutine 与 GMP 调度、channel 与 sync 包、GC 与内存分配、逃逸分析、接口与反射的底层、常见并发 bug（泄漏、竞态、死锁）。语言本身简单，所以面试官不太考"语法知不知道"，而在意三件事：一是并发代码写得对不对——能否指出一段代码里的 goroutine 泄漏或数据竞争、知不知道 context 应该怎么传；二是有没有用 pprof 真实定位过 CPU、内存、阻塞问题；三是工程习惯——错误处理、依赖注入、包组织、测试、优雅退出这些在 Go 社区有强约定的东西是否内化。

校招侧重语言基础与并发模型：slice 与 map 的底层、defer 的执行顺序与坑、goroutine 与线程的差别、channel 的阻塞语义、手写 worker pool 或带超时的并发请求。社招侧重线上问题与设计：goroutine 数量涨到十万怎么排查、GC 频繁怎么优化、gRPC 连接与超时怎么管、如何设计一个可控的并发访问下游的组件。近两年一线公司会直接给代码片段问"这段有什么问题"，并追 Go 1.21+ 的新特性（泛型实践、结构化日志、PGO）是否用过。

怎么问才像这个方向的面试官：
- 架构与方法论（缓存一致性、消息队列、限流降级）交给 backend 包，本包专注 Go 运行时、并发原语与工程实践。
- 并发题优先给代码：让候选人现场写或找 bug，追每个 goroutine 的生命周期、每个 channel 的关闭方，不接受"大概是这样"。
- 性能题必须落到 pprof：候选人说了任何"优化"，追用的哪个 profile、看到了什么、benchmark 前后数字。
- 结合项目规模：几百 QPS 的服务不追 GOMEMLIMIT 与 PGO，但必须会写正确的 context 传递与优雅退出；基础设施类项目要追调度器与内存分配细节。
- 2026 年 Go 面试的新追问：GreenTea 一类新 GC 与容器感知的 GOMAXPROCS 改变了老的调优结论，要能说清默认值为什么变、什么场景还要手调；Go 作为云原生与 Agent 网关的主力语言，会追 context 取消如何贯穿到下游模型调用与流式转发。

## 项目 / 实习怎么深挖
…

Read the whole file at its exact version.

How to install

Latest version

mdr add yuecao365/offercome/go@git:20260920.a4843f5

Exact content

mdr add yuecao365/offercome/go@sha256:64edddb774641ea6

Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.

Badge

[![mdr](https://markdownregistry.com/badge/art_pyltp4gq57zzdnjs.svg)](https://markdownregistry.com/a/art_pyltp4gq57zzdnjs)

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Versions

version	committed	commit	size	audit
git:20260920.a4843f5 latest	2026-09-20	a4843f5	12,866 B	A	view

Audit of the latest version

A 17 of 17 checks passed. Deterministic, no model, same answer every run.

pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (12866 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag

Source

GitHub

yuecao365/offercome · 23 stars · license MIT · pushed 2026-09-23 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_pyltp4gq57zzdnjs
GET https://markdownregistry.com/api/v1/resolve?ref=yuecao365/offercome/go
GET https://markdownregistry.com/api/v1/blob/64edddb774641ea607e7ba1fdcd28be37bbebf548a343f9aada31467ea70fd62

Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

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agent-runtime skill

yuecao365/offercome · src/lib/mock-interviews/skills/agent-runtime/SKILL.md · Agent 运行时深挖：循环与事件、工具协议与沙箱、子 agent、预算终止、恢复、输出契约。