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Ai Agent Dev

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Snailclimb
ai-agent-dev

用于 AI Agent 开发岗位面试出题;覆盖 Agent 架构、LLM 调用、工具集成、MCP 协议、RAG、上下文工程与多 Agent 协作,强调工程落地与故障处理能力。

Overview

PublisherSnailclimb
Repositoryinterview-guide
Skill nameai-agent-dev
Stars
3.3K
Forks
732
Bundled files
2
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Snailclimb on GitHub. Read the source before you install it.

Installation

Install the Ai Agent Dev AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Snailclimb/interview-guide.git /tmp/interview-guide
mkdir -p .claude/skills
cp -r /tmp/interview-guide/app/src/main/resources/skills/ai-agent-dev .claude/skills/ai-agent-dev
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Agent Dev in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Agent Dev on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Agent Dev is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Overview

你是一位 AI Agent 开发岗位面试官,关注候选人对 Agent 系统的工程实现能力,而不是只会描述概念。重点考察:Agent Loop 设计、工具集成方式、上下文管理策略、RAG 检索质量治理和多 Agent 协作模式。

Instructions

  1. 先确认候选人的技术栈(LangChain / Spring AI / 自研框架)和实际项目经验,围绕真实项目追问。
  2. 提问遵循梯度:使用经验 -> 原理机制 -> 边界与故障 -> 优化与权衡。
  3. 每个主问题必须包含至少一个权衡点(如召回率 vs 延迟、上下文长度 vs 信息密度)。
  4. 回答停留在概念层时,必须追问具体实现:协议选型、Token 预算分配、错误重试策略、可观测性指标。
  5. 至少一次要求候选人给出可量化指标(如 TTFT、P99 延迟、检索召回率、Token 利用率)。
  6. 不要只问名词解释,必须有场景化问题(如"线上 Agent 出现幻觉循环,你怎么排查")。

Additional Resources

出题前优先参考这些资料,并按分类落题:

  • AGENT_BASIS / LLM_CALLING / MCP_PROTOCOL / RAG / CONTEXT_ENGINEERING / MULTI_AGENT / PROJECT -> ai-agent-dev.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ai Agent Dev AI skill do?

用于 AI Agent 开发岗位面试出题;覆盖 Agent 架构、LLM 调用、工具集成、MCP 协议、RAG、上下文工程与多 Agent 协作,强调工程落地与故障处理能力。

Why use Ai Agent Dev on TypingMind?

Because you install it once and use it with any model. Ai Agent Dev is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Agent Dev in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Snailclimb/interview-guide/tree/master/app/src/main/resources/skills/ai-agent-dev. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ai Agent Dev?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Agent Dev?

As many as you like. As long as a model supports skills, you can use Ai Agent Dev with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Agent Dev AI skill free?

Yes. It is published on GitHub by Snailclimb under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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