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/content-review/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/content-review)<a href="https://agentmods.dev/skills/ranxi2001/zero2agent/content-review"><img src="https://agentmods.dev/badge/skills/ranxi2001/zero2agent/content-review/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/content-review"><img src="https://agentmods.dev/badge/skills/ranxi2001/zero2agent/content-review.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 | $0.00112 | $0.00949 |
| Opus 5.5 | $0.00045 | $0.00380 |
| Sonnet 5.5 | $0.00022 | $0.00190 |
| Haiku 4.5 | $0.00011 | $0.00095 |
Grade A, and why
content-review 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.
What it actually says
content-review：文章内容审查
本技能用于对 zero2Agent 项目中的 Markdown 文章进行结构化质量审查。
审查维度（7 项）
1. Frontmatter 完整性
layout: default是否存在title、description、eyebrow是否均已填写eyebrow格式是否正确（如Agent Basic / 05）description长度是否在 10–30 字之间
2. 开篇质量（问题优先）
- 开头是否先说"为什么这个问题值得关注"，而非直接给定义
- 是否引出了读者可能有的误解或常见困惑
- 前 3 段是否能让读者产生"这跟我有关"的感觉
3. 工程视角
- 概念解释是否落脚到"它在系统里扮演什么角色"
- 是否有具体的系统行为描述，而不只是抽象定义
- 是否提到了真实工程场景中的约束或权衡
4. 避免框架崇拜
- 是否出现了对某个框架的无条件推荐
- 每次提到框架时是否说明了"它解决什么问题"和"它的代价"
- 是否有"X 是最佳实践"这类不加论证的断言
5. 暴露真实复杂度
- 是否主动提到了容易踩坑的地方
- 是否区分了"Demo 能跑"和"生产可用"的差距
- 是否对失败场景有基本描述
6. 语言和结构
- 全文是否使用中文（代码块、路径、专有名词除外）
- 段落是否简洁，避免废话和修饰语堆砌
- 是否合理使用了
# ## ###标题层级 - 是否合理使用了代码块（
text或具体语言）展示流程/示例
7. 文末导航
- 是否有"下一篇建议继续看："的链接
- 链接路径是否使用相对路径 +
index.html
输出格式
审查结果按如下结构输出：
## 审查报告：{文章标题}
**整体评分**：✅ 通过 / ⚠️ 需改进 / ❌ 有明显问题
### 1. Frontmatter 完整性
状态：✅ / ⚠️ / ❌
说明：{具体发现，若通过则简写"符合要求"}
### 2. 开篇质量
状态：…
说明：…
### 3. 工程视角
…（以此类推）
---
## 改进建议
{按优先级排列的具体修改建议，每条说明原因}
1. **[高优先级]** {问题描述} → 建议：{具体怎么改}
2. **[中优先级]** …
3. **[低优先级]** …
执行步骤
-
定位文章：如果用户没有指定路径，根据对话上下文判断要审查哪篇文章。如果不明确，询问用户。
-
读取文章：用 Read 工具读取对应的
index.md。 -
逐维度审查：按上述 7 个维度逐一检查，记录发现。
-
输出报告：按输出格式生成报告，改进建议要具体到"第几段、哪句话、怎么改"，不要泛泛而谈。
-
可选：批量审查：如果用户要求审查整个模块，依次读取所有文章并汇总报告，最后给出模块级别的总结（哪些维度普遍偏弱）。
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 · 90 lines · 112 tokens per session scan A 920b3f89824d
content-review is a skill published in the GitHub repository ranxi2001/zero2Agent (1,026 stars, last pushed yesterday), licensed MIT. It adds 112 tokens to every session and 949 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.
content-review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| content-reviewranxi2001/zero2Agent | Skill | 1.0k | yesterday | MIT | A | 112 | 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 |
| check-modelginlix-ai/LangAlpha | Skill | 2.2k | yesterday | Apache-2.0 | A | 72 | Claude Code, Codex |