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Agent Skill · 研究检索

aeo-optimization(AE 优化)

aeo-optimization 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

aeo-optimization 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 19,328。

总安装 5,657周安装 243GitHub Stars 590下载 1,983核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:aeo-optimization(AE 优化)
来源仓库:https://github.com/alinaqi/claude-bootstrap
仓库路径:skills/aeo-optimization
安装命令:
npx skills add https://github.com/alinaqi/claude-bootstrap --skill aeo-optimization
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/alinaqi/claude-bootstrap --skill aeo-optimization

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

AI 引擎优化技能帮助品牌内容在 ChatGPT、Claude 等平台获得更多引用机会。适用于希望提升 AI 摘要中品牌曝光度的营销与技术团队。基于 HubSpot AEO 指南,提供结构化内容优化建议与最佳实践参考。使用时需注意避免凭空添加字段,应结合现有代码或接口样例进行验证。

aeo-optimization 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/aeo-optimization

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/alinaqi/claude-bootstrap --skill aeo-optimizat…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

aeo-optimization 怎么安装?

在终端执行 npx skills add https://github.com/alinaqi/claude-bootstrap --skill aeo-optimization。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

aeo-optimization 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

aeo-optimization 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

aeo-optimization 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

aeo-optimization 用的人多吗?

公开数据是累计安装 5,657 次,近一周安装 243 次,下载 1,983 次,GitHub 590 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

aeo-optimization 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
aeo-optimization

AE 优化 · 本页

5,657590只读MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

AI Engine Optimization (AEO) Skill

Purpose: Optimize content for AI engines (ChatGPT, Claude, Perplexity, Google AI Overviews) so your brand gets cited in AI-generated answers.

Source: Based on HubSpot's AEO Guide and industry best practices.


Why AEO Matters Now

┌────────────────────────────────────────────────────────────────┐
│  THE GREAT DECOUPLING                                          │
│  ────────────────────────────────────────────────────────────  │
│  Impressions ≠ Clicks anymore.                                 │
│  AI engines compile answers from multiple sources.             │
│  More buyer journey happens inside chat experiences.           │
│  58% of Google searches = zero clicks (AI overviews).          │
├────────────────────────────────────────────────────────────────┤
│  THE OPPORTUNITY                                               │
│  ────────────────────────────────────────────────────────────  │
│  Shape what AI engines say about your category and product.    │
│  Get cited as the authoritative source.                        │
│  Best answer > Best page ranking.                              │
└────────────────────────────────────────────────────────────────┘

Key Stats:

  • 70% of consumers use ChatGPT for searches
  • 47% of Google queries show AI overviews
  • Average ChatGPT prompt: 23 words (vs 4.2 for Google)
  • AEO market: $886M (2024) → $7.3B (2031)

How AI Engines Choose Answers

AI engines use three main signals to select content for answers:

1. Consensus

Facts that appear across multiple credible sources get trusted and reused.

How to build consensus:

  • Repeat key facts consistently across your own pages
  • Use same terminology as industry leaders
  • Link to and from authoritative external sources
  • Create internal content clusters that reinforce each other

2. Information Gain

Net-new insight beats generic advice. AI engines prefer content that adds value.

How to add information gain:

  • Original research and data
  • Concrete examples with specifics
  • Clear point of view (not fence-sitting)
  • Expert quotes with credentials
  • Case studies with metrics

3. Entities & Structure

Clear entities and tidy structure reduce ambiguity and boost quotability.

How to optimize structure:

  • Use semantic triples (Subject → Verb → Object)
  • Clear headings with entity names
  • Schema markup (Article, FAQ, Product)
  • Short, scannable paragraphs (2-4 sentences)

Semantic Triples (Critical for AEO)

What they are: Compact facts that AI engines (and humans) can't misread.

Pattern: [Subject] [verb] [object].

Examples

✅ GOOD (clear triples):
- HubSpot CRM syncs contact and company data.
- Lead Scoring assigns priority based on engagement.
- Workflows trigger email sequences from events.

❌ BAD (vague, no clear entity):
- The system helps with various tasks.
- It can do many things for users.
- This improves overall performance.

Triple Checklist

For every key claim, ask:

  • Is the subject a clear entity (product, feature, brand)?
  • Is the verb specific and active?
  • Is the object concrete and measurable?

Paragraph Pattern (Feature → How → Outcome)

Every substantive paragraph should follow this structure:

[Feature] helps [User/Role] with [Job].
It [mechanism/inputs] to [process].
Teams see [metric/result] in [timeframe/context].

Triples:
- [Subject] [verb] [object].
- [Subject] [verb] [object].

Example

Lead Scoring helps sales teams prioritize prospects. It combines
page views, email engagement, and firmographic data to assign a
numeric score, then auto-enrolls high scorers into follow-up
sequences. Reps focus on qualified accounts and book 40% more
meetings.

- Lead Scoring assigns scores from engagement data.
- High scorers trigger automated follow-up sequences.

Page Templates

Template 1: Category Explainer

Goal: Define the category, tie it to your product, earn citations.

# What is [Category]? — [1-2 line value promise]

## What is [Category]? (~80 words)
[Plain definition in everyday language. Name adjacent entities.]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## Why it matters now (~60 words)
[One paragraph. Mention shift to answers over links; tie to buyer outcomes.]

## How to apply it (3-5 bullets)
- [Action 1]
- [Action 2]
- [Action 3]

## FAQ
**Q: [Question]?**
A: [~1 sentence answer]

**Q: [Question]?**
A: [~1 sentence answer]

**Q: [Question]?**
A: [~1 sentence answer]

---
**Links:** [Category hub] | [Product/Feature] | [Credible source 1] | [Credible source 2]
**CTA:** [Demo / Template / Signup]
**Schema:** Article + FAQ. Author + last updated.

Template 2: Product & Feature Page

Goal: Clarify capability, fit, and next step; reinforce category linkage.

# [Product/Feature] — [Outcome in 3-5 words]

**[Product/Feature] enables [Outcome] for [User/Role].**

## [Feature Area 1]
[2-4 sentences using Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## [Feature Area 2]
[2-4 sentences using Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## [Feature Area 3]
[2-4 sentences using Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## FAQ
**Q: [Question]?**
A: [~1 sentence]

**Q: [Question]?**
A: [~1 sentence]

**Q: [Question]?**
A: [~1 sentence]

---
**Links:** Back to [Category Explainer] | Forward to [Demo/Trial]
**Proof:** [Benchmark/Analyst/Customer proof]
**Notes:** Requirements/limits (pricing tier, integrations)
**Schema:** Article + FAQ. Author + last updated.

Template 3: Comparison / Alternatives Page

Goal: Help readers decide with clear criteria; earn fair citations.

# [Product] vs. [Alternative] — Which fits [Use case]?

## Comparison Table

| Criterion | [Product] | [Alt A] | [Alt B] | Source |
|-----------|-----------|---------|---------|--------|
| [Feature/Limit] | [value] | [value] | [value] | [link] |
| [Requirement] | [value] | [value] | [value] | [link] |
| [Best for] | [value] | [value] | [value] | [link] |

*Source-back all claims in the table or footnotes.*

## Fit Statements

1. **[Product]** suits [Team/Use case] when [Condition].
2. **[Alt A]** fits [Team/Use case] when [Condition].
3. **[Alt B]** works for [Team/Use case] when [Condition].

---
**Links:** [Category Explainer] | [Feature pages]
**CTA:** [Try / Demo / Talk to Sales]
**Schema:** Article. Author + last updated.

Template 4: Use Case / Industry Page

Goal: Connect product to outcomes in a context readers recognize.

# [Industry/Use Case] — [Outcome KPI]

**Teams reduce [Metric] by [Y%] in [Timeframe].**

## Mini Case Study
[Company/Role] used [Product/Feature] to [Action], resulting in
[Metric improvement] within [Timeframe].

## How It Works

### [Feature 1]
[Feature → How → Outcome paragraph]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

### [Feature 2]
[Feature → How → Outcome paragraph]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

## Who Uses This
**Roles:** [Role 1], [Role 2], [Role 3]
**Workflows:** [Workflow 1], [Workflow 2]
**Integrations:** [Integration 1], [Integration 2]

---
**Links:** [Product/Feature pages] | [Supporting blog]
**CTA:** [Industry template / Demo variant]
**Schema:** Article. Author + last updated.

Template 5: Supporting Blog Post

Goal: Add information gain and support your content cluster.

# [Topic] — [Specific promise]

## Opening (~60-80 words)
[State the problem. Align terminology with Category Explainer. Preview outcome.]

## [Section 1 Heading] (~120 words max)
[Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

**Internal link:** [Related page]
**External citation:** [Credible source]

## [Section 2 Heading] (~120 words max)
[Feature → How → Outcome]

Triples:
1. [Subject] [verb] [object].
2. [Subject] [verb] [object].

**Internal link:** [Related page]
**External citation:** [Credible source]

## Key Takeaway
[1-2 lines summarizing the main point]

**CTA:** [Single primary action]

---
**Schema:** Article. Author + last updated.

Site-Wide Trust Signals

Required on Every Page

ElementImplementation
Schema markupArticle + FAQ (if FAQ exists)
Author attributionName, bio, credentials, photo
Last updated dateVisible, machine-readable
Internal links3-5 per page (upstream/downstream)
External citations1-2 credible sources per section
Single CTADemo, template, or signup (repeated once near end)

Schema Implementation

<!-- Article Schema -->
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "[Page Title]",
  "author": {
    "@type": "Person",
    "name": "[Author Name]",
    "url": "[Author Bio URL]"
  },
  "datePublished": "[ISO Date]",
  "dateModified": "[ISO Date]",
  "publisher": {
    "@type": "Organization",
    "name": "[Company]",
    "logo": "[Logo URL]"
  }
}
</script>

<!-- FAQ Schema (if FAQ section exists) -->
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "[Question 1]",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "[Answer 1]"
      }
    },
    {
      "@type": "Question",
      "name": "[Question 2]",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "[Answer 2]"
      }
    }
  ]
}
</script>

Content Cluster Architecture

                    ┌─────────────────────┐
                    │  Category Explainer │
                    │   "What is AEO?"    │
                    └──────────┬──────────┘
                               │
        ┌──────────────────────┼──────────────────────┐
        │                      │                      │
        ▼                      ▼                      ▼
┌───────────────┐    ┌───────────────┐    ┌───────────────┐
│ Product Page  │    │ Product Page  │    │ Product Page  │
│  "Feature A"  │    │  "Feature B"  │    │  "Feature C"  │
└───────┬───────┘    └───────┬───────┘    └───────┬───────┘
        │                    │                    │
        ▼                    ▼                    ▼
┌───────────────┐    ┌───────────────┐    ┌───────────────┐
│  Blog Post    │    │  Use Case     │    │  Comparison   │
│  (supports)   │    │  (industry)   │    │  (vs. alt)    │
└───────────────┘    └───────────────┘    └───────────────┘

Linking Rules:

  • Category Explainer links DOWN to all product pages
  • Product pages link UP to Category Explainer
  • Product pages link ACROSS to related features
  • Blog posts link UP to Product pages
  • Comparison pages link to Category Explainer + relevant Product pages

AEO Writing Checklist

Per-Paragraph Checklist

  • Follows Feature → How → Outcome pattern
  • Contains 2-4 sentences (scannable)
  • Includes 1-2 semantic triples
  • Names specific entities (not vague "it" or "this")
  • Uses active voice verbs

Per-Section Checklist

  • Has 1 internal link (upstream or downstream)
  • Has 1 external citation (credible source)
  • Section heading names an entity
  • ~120 words max

Per-Page Checklist

  • H1 contains primary entity + value promise
  • Opening claim is a semantic triple
  • 3-5 internal links total
  • 1-2 external citations total
  • Mini-FAQ with 3 questions (if applicable)
  • Single primary CTA
  • Schema markup (Article + FAQ)
  • Author name + bio link
  • Last updated date visible

Site-Wide Checklist

  • Category Explainer exists for each key category
  • Product pages link back to Category Explainer
  • Content cluster architecture documented
  • Author bio pages exist with credentials
  • Consistent terminology across all pages

Measuring AEO Success

Key Metrics

MetricHow to Track
AI citationsManual checks in ChatGPT, Claude, Perplexity
Brand mentions in AISearch "[brand] + [category]" in AI engines
Share of answerHow often you're cited vs competitors
LLM trafficGA4 referral from chatgpt.com, claude.ai, perplexity.ai
Impressions-to-clicks gapGSC impressions vs actual clicks

Tools

  • HubSpot AEO Grader - Grade your brand's AI visibility
  • Google Analytics 4 - Track LLM referral traffic
  • Google Search Console - Monitor impressions vs clicks gap
  • Manual AI queries - Regularly test your brand in AI engines

Common AEO Mistakes

MistakeFix
Vague language ("it helps with things")Use specific entities and triples
No clear structureUse Feature → How → Outcome
Missing schemaAdd Article + FAQ schema
No author attributionAdd author name, bio, credentials
Generic contentAdd original data, examples, POV
Orphan pagesLink into content cluster
Fence-sitting ("it depends")Take a clear position
No external citationsAdd 1-2 credible sources per section

AEO vs Traditional SEO

AspectTraditional SEOAEO
GoalRank on page 1Get cited in AI answers
Success metricClick-through rateShare of answer
Content focusKeywordsEntities + facts
StructureHeaders for scanningTriples for extraction
LinksBacklinks for authorityCitations for consensus
UpdatesPeriodic refreshContinuous accuracy

Quick Reference

Semantic Triple Pattern

[Entity/Product] [active verb] [concrete object/result].

Paragraph Pattern

[Feature] helps [User] with [Job].
It [mechanism] to [process].
Teams see [result] in [timeframe].

Page Minimums

  • 3-5 internal links
  • 1-2 external citations per section
  • 3 FAQ questions with schema
  • Author + last updated
  • Single CTA

Content Hierarchy

  1. Category Explainer (top)
  2. Product/Feature pages (middle)
  3. Use case / Comparison / Blog (supporting)

平台分布

Claude Code

27.11%
按下载量换算538

OpenCode

25.59%
按下载量换算507

Antigravity

18.42%
按下载量换算365

Cursor

11.53%
按下载量换算229

Gemini CLI

7.87%
按下载量换算156

Codex

3.34%
按下载量换算66

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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