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/new-article/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/new-article)<a href="https://agentmods.dev/skills/ranxi2001/zero2agent/new-article"><img src="https://agentmods.dev/badge/skills/ranxi2001/zero2agent/new-article/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/new-article"><img src="https://agentmods.dev/badge/skills/ranxi2001/zero2agent/new-article.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 | $0.00124 | $0.01467 |
| Opus 5.5 | $0.00050 | $0.00587 |
| Sonnet 5.5 | $0.00025 | $0.00293 |
| Haiku 4.5 | $0.00012 | $0.00147 |
Grade A, and why
new-article 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
new-article：创建新文章
本技能用于在 zero2Agent 项目中创建符合项目风格的新学习文章。
项目约定
目录结构
{module-dir}/
└── {NN}-{slug}/
└── index.md
{NN}是两位数编号，如01、09、10{slug}是英文小写、用连字符连接，如tool-calling-basics- 每篇文章独占一个子目录，主文件统一命名为
index.md
已有模块目录
| 模块 | 目录 | 已有文章数 |
|---|---|---|
| Agent Basic | learn-agent-basic/ |
08 篇 (01–08) |
| Agent Survey | learn-agent-survey/ |
13 篇 (01–13) |
| Agent Training | learn-agent-training/ |
06 篇 (01–06) |
| Agent Interview | learn-agent-interview/ |
08 篇 (01–08)，含面经实录 |
| LangGraph | learn-langgraph/ |
07 篇 (01–07) |
| Claude Code | learn-claude-code/ |
12 篇 (01–12) |
| SDK Frameworks | learn-sdk-frameworks/ |
04 篇 (01–04) |
| OpenClaw | learn-openclaw/ |
09 篇 (01–09) |
| Final Project | final-project/ |
0 篇 (占位符) |
Frontmatter 格式
---
layout: default
title: {文章标题（中文）}
description: {一句话描述，10–25 字}
eyebrow: {Module Name} / {NN}
---
eyebrow 示例：Agent Basic / 09、LangGraph / 01
文章写作风格（必须遵守）
zero2Agent 的读者是懂代码、懂深度学习基础的开发者，但对 Agent 工程实践还不熟悉。写作时：
- 问题优先：先说“为什么要关心这个问题”，再讲概念和方案。不要一上来就定义。
- 工程视角：解释概念时，要说清楚它在系统里扮演什么角色，而不是给出教科书定义。
- 避免框架崇拜：不要把某个框架讲成“最佳答案”，要讲清楚它解决什么问题、有什么代价。
- 暴露真实复杂度：要主动提到“这里容易踩坑”、“Demo 能跑但生产不行”。
- 精炼、不废话：不加不必要的修饰语，每个段落有实际内容。
- 使用代码块和图表：需要展示执行流程时优先用
```text代码块而不是大段描述。需要正式图表时默认使用```mermaid，包括多阶段管线、对比关系、架构分层和复杂流程，并按mermaid-check验证兼容性。仅当 Mermaid 无法可靠表达所需布局，或用户明确要求可编辑 Draw.io 画布时，才使用drawio-skill生成.drawio源文件和渲染资产。 - 文末导航：在最后用
下一篇建议继续看：+ 链接收尾（或说明尚无后续）。
文章结构模板
---
layout: default
title: {标题}
description: {一句话描述}
eyebrow: {Module} / {NN}
---
# {标题}
{开篇：1–3 句话，说明这个话题在实际工程中为什么重要 / 常见误解是什么}
## {核心概念或问题拆解}
{正文……}
## {深入一层：机制 / 设计原则 / 常见坑}
{正文……}
## {实践建议 或 典型误区}
{正文……}
## 小结
{用 3–5 个 bullet 提炼核心观点，不重复正文措辞}
下一篇建议继续看：
- [{下一篇标题}]({相对路径}/index.html)
面试模块特殊格式（learn-agent-interview）
该模块有两类文章，格式不同：
维度拆解文章（01–07）
按考察维度分类，每道题用“新手答 vs 高手答”对比格式：
## Q：{面试题}
> 来源：{公司/岗位}
**新手答**："{浅层回答}"
**高手答**：
{深度回答，分层递进，带具体方案}
**差距在哪**：{分析新手和高手答案的差距，点出面试官真正在考什么}
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 First seen · 145 lines · 124 tokens per session scan A 041db3075d7f
new-article is a skill published in the GitHub repository ranxi2001/zero2Agent (1,026 stars, last pushed yesterday), licensed MIT. It adds 124 tokens to every session and 1,467 once invoked, about $0.0005 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.
new-article compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| new-articleranxi2001/zero2Agent | Skill | 1.0k | yesterday | MIT | A | 124 | 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 |