zero2Agent is a tutorial repository that teaches software developers how to build AI agents, covering core concepts, model APIs, tools, memory, frameworks, implementation, and complete projects. It is intended for programmers learning agent engineering and for candidates preparing for agent-related technical interviews.
Borrowing it
Nothing to install: this file belongs to ranxi2001/zero2Agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ranxi2001/zero2Agent/main/.agents/skills/classify-interview-questions/SKILL.mdgit clone --depth 1 https://github.com/ranxi2001/zero2AgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ranxi2001/zero2agent/classify-interview-questions)<a href="https://agentmods.dev/skills/ranxi2001/zero2agent/classify-interview-questions"><img src="https://agentmods.dev/badge/skills/ranxi2001/zero2agent/classify-interview-questions/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ranxi2001/zero2agent/classify-interview-questions"><img src="https://agentmods.dev/badge/skills/ranxi2001/zero2agent/classify-interview-questions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.05342 |
| Opus 5.5 | $0.00044 | $0.02137 |
| Sonnet 5.5 | $0.00022 | $0.01068 |
| Haiku 4.5 | $0.00011 | $0.00534 |
Grade A, and why
classify-interview-questions scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
classify-interview-questions：面试题分类分发
将新面试题按考察维度分类，拆分后加入已有题库。不新建独立面经实录文章。
两个目标仓库
- Agent、LLM、RAG、训练、多模态、AI Coding 与 AI 工程题：当前
zero2Agent/learn-agent-interview/。 - Java/Go/Python、JVM、并发、操作系统、网络、数据库、缓存、消息队列、分布式、前端与通用工程八股：相邻
../zero2Leetcode/_includes/interview-seasons/2026/summer.md。 - 算法/手撕题只进入 zero2leetcode 的算法题单，不计入传统八股总数；题干不完整时排除，不补造。
- 机器召回使用本 Skill 的
question-index.json；question-index.md是人工维护源。每次修改 Markdown 后必须重建 JSON 并运行 stale check。 question-frequency.json是 Agent 题单频次事实源。frequency等于evidence中可归因的独立面经出现次数；题库汇总、“高频题”等不可追溯标签不计数。正文只在现有最小主题组内按频次降序排列，同频按稳定的firstSeenOrder排列。- 两个仓库分别检查 worktree、计数、提交和推送。不得把一个仓库的 commit 混入另一个。
来源与超链接契约
来源链接是题库可信度的一部分。以后新增题、增强已有题和调研答案都按以下标准维护，不只写不可核验的公司名或“高频题”。
面试题证据
-
有公开原文时，正文
> 来源：使用可点击的 Markdown 链接，链接文字保留公司、岗位和轮次，例如：> 来源：[阿里云 Agent Infra 一面](https://www.nowcoder.com/feed/main/detail/<id>) -
链接必须直达原始面经文章或用户指定的一手页面，不使用搜索结果页、信息流首页、聚合转载、短链或无关主页。多个独立来源分别给链接，不能把多个 URL 藏在一个笼统标签里。
-
同一来源要贯穿三处：正文来源行、
question-index.md的题目来源、question-frequency.json的 evidence。人工索引保留 Markdown 链接；频次 JSON 中每个独立原文 URL 单独占一个 evidence，同一核心题里的同一 canonical URL 只计一次。 -
增强已有题时保留原有链接，再追加新来源或追问链接；不要把已经可点击的来源降级成纯文本。
-
URL 只是可追溯入口，不自动证明内容是一手面经。仍需按真实面试过程、公司/岗位/轮次和问题上下文判断是否可归因，转载、题库汇总和营销内容仍不计频次。
-
用户明确授权的本地题库或私有材料可以作为来源，但没有公开 URL 时必须写明“用户授权题库（无公开 URL）”，不得伪造链接，也不得把本机绝对路径、私有下载地址或凭证写进站点。若能定位到公开原文，先验证内容一致再补回原始链接。
-
无公开 URL、又没有用户明确授权或其他可归因证据的题目，不进入频次事实源。
答案事实依据
- 协议、框架、模型、Kubernetes、AIOps 等会变化或容易混淆的事实，优先查当前一手资料，并在支持该结论的正文附近直接链接官方文档、官方仓库、标准或论文，不只在文末列一个泛化“参考资料”。
- 链接应指向支持具体结论的页面；官方资料没有直接支持时，明确标注这是工程推断或经验判断，不借链接制造过度确定性。
- 答案实质依赖外部资料时同步更新
THIRD_PARTY_NOTICES.md。独立改写答案，不复制外部文章的长段落、图表或受版权保护内容。
链接验收
- 发布前验证新增链接可访问、页面标题与标注一致、没有误链到评论区或推荐页；登录受限页面至少确认 URL 是原始文章地址。
- 核对每个新增或增强题的正文来源、人工索引和频次 evidence，不允许其中一处有 URL、另两处丢失 URL。
- 汇报时增加来源覆盖：公开原文链接数、用户授权但无公开 URL 的来源数、无法归因而排除的来源数。
维度分类表
| 编号 | 维度 | 目录 | 典型考察内容 |
|---|---|---|---|
| 01 | 架构选型 | 01-architecture-design/ |
ReAct/Plan-Execute/ToT、Agent 组成、设计范式、规划器 |
| 02 | 工具管理 | 02-tool-management/ |
参数校验、工具路由、多工具调度、Mock 生成 |
| 03 | 容错与鲁棒性 | 03-fault-tolerance/ |
超时处理、误操作防范、幻觉治理、失败恢复 |
| 04 | 记忆与上下文 | 04-memory-context/ |
长对话、模糊需求、上下文污染、长短期记忆、to-do list |
| 05 | 评估与全局观 | 05-eval-and-vision/ |
量化评估、落地挑战、AI 工具价值/边界、行业认知 |
| 06 | 多智能体协作 | 06-multi-agent-collab/ |
角色分工、通信机制、冲突仲裁、记忆共享 |
| 07 | 工程化踩坑 | 07-engineering-pitfalls/ |
死循环、状态丢失、成本控制、AI Coding 实践、工具使用 |
| 08 | Prompt 工程 | 08-prompt-engineering/ |
模板构建、Skills 机制、好/差 Prompt 区别、框架创新 |
| 09 | RAG 与检索 | 09-rag-retrieval/ |
chunk 设计、查询改写、召回精排、Embedding/ReRank 微调 |
| 10 | 训练与模型 | 10-training-and-data/ |
数据清洗、LoRA、PPO/DPO/GRPO、位置编码、归一化、量化部署、多模态 |
| 11 | AI 代码测试 | 11-ai-code-testing/ |
覆盖率插桩、前置分析、代码过滤 |
| 12 | 业务 AI 工程 | 12-business-ai-engineering/ |
业务需求拆解、方案选型、效果评估、智能客服与业务落地 |
| 13 | 简历项目拷打 | 13-project-deep-dive/ |
项目部署、框架选型、意图识别、工具设计、知识库构建、性能优化 |
| 14 | 公司偏好（派生页） | 14-company-preferences/ |
从各维度来源统计公司考察偏好，不直接写入新题 |
| 15 | Agent 概念 | 15-agent-concepts/ |
Harness/Context Engineering、Vibe Coding、MCP、Skills 等概念辨析 |
| 16 | Agent Infra | 16-agent-infra/ |
Runtime、Checkpoint、幂等、Sandbox、Kubernetes、调度与可观测 |
| 17 | AI Infra | 17-ai-infra/ |
分布式训练、LLM Serving、GPU 调度、模型发布与 AIOps |
What ships with it
18 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- question-frequency.json 596 KB
- question-index.json 1077 KB
- question-index.md 474 KB
- scripts/batch_extract_and_recall.py 12 KB runs code
- scripts/build_question_index_json.py 4.1 KB runs code
- scripts/build_recall_queues.py 8.7 KB runs code
- scripts/extract_and_recall.py 32 KB runs code
- scripts/question_frequency.py 6.7 KB runs code
- scripts/recall_similar_questions.py 14 KB runs code
- scripts/sort_questions_by_frequency.py 11 KB runs code
- scripts/source_gate.py 5.8 KB runs code
- scripts/sync_question_frequency.py 4.6 KB runs code
- tests/test_build_recall_queues.py 3.0 KB runs code
- tests/test_extract_and_recall.py 8.6 KB runs code
- tests/test_question_frequency.py 7.5 KB runs code
- tests/test_recall_similar_questions.py 6.9 KB runs code
- tests/test_sort_questions_by_frequency.py 2.3 KB runs code
- tests/test_source_gate.py 3.4 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago Changed · -9 lines · +5 tokens per session scan B → A e975eda01b70
- 2mo ago First seen · 296 lines · 104 tokens per session scan B f156eed3b457
classify-interview-questions is a skill published in the GitHub repository ranxi2001/zero2Agent (1,026 stars, last pushed yesterday), licensed MIT. It adds 109 tokens to every session and 5,342 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
classify-interview-questions compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| classify-interview-questionsranxi2001/zero2Agent | Skill | 1.0k | yesterday | MIT | A | 109 | Claude Code, Codex |
| ace-rsi-toolkitPrism-Shadow/penguin-harness | Skill | 3.2k | yesterday | Apache-2.0 | A | 69 | Claude Code, Codex |
| interactive-dashboardginlix-ai/LangAlpha | Skill | 2.2k | yesterday | Apache-2.0 | A | 21 | Claude Code, Codex |
| dcf-modelginlix-ai/LangAlpha | Skill | 2.2k | yesterday | Apache-2.0 | A | 56 | Claude Code, Codex |
| comps-analysisginlix-ai/LangAlpha | Skill | 2.2k | yesterday | Apache-2.0 | A | 48 | Claude Code, Codex |
| company-profileginlix-ai/LangAlpha | Skill | 2.2k | yesterday | Apache-2.0 | A | 68 | Claude Code, Codex |
| html-reportginlix-ai/LangAlpha | Skill | 2.2k | yesterday | Apache-2.0 | A | 29 | Claude Code, Codex |