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Agent Skill · 研究检索

aeo-system(空气动力学系统)

aeo-system 用于查找、检索和筛选相关信息,适合在 OpenClaw 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-system 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 9,750。

总安装 13,660周安装 558GitHub Stars 公开资料未说明下载 4,375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-0
版本
1.0.0

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-system(空气动力学系统)
来源仓库:https://github.com/batsirai/aeo-system
安装命令:
openclaw skills install aeo-system
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。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install aeo-system

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。

aeo-system 实现答案引擎优化,提升品牌在 AI 推荐中的排名。支持 AEO 审核、意图图谱构建与位置追踪。整合多维度信号优化品牌可见性。建议定期运行以监控变化趋势。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

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

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 OpenClaw;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 openclaw skills install aeo-system;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

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

aeo-system 怎么安装?

在终端执行 openclaw skills install aeo-system。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

aeo-system 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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-system 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

aeo-system 是免费的吗?

来源标注的许可证是 MIT-0。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-system 用的人多吗?

公开数据是累计安装 13,660 次,近一周安装 558 次,下载 4,375 次。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-system 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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 OpenClaw,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。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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-system

空气动力学系统 · 本页

13,660—接触敏感数据MIT-0有提醒

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

name
aeo-system
description
Answer Engine Optimization — get AI assistants to recommend your brand. Run AEO audits, build Answer Intent Maps, track AI recommendation positions, and maintain a 7-layer AEO infrastructure for any brand or product category.
requiredEnv
permissions
source
url
https://github.com/Batsirai/carson-skills
author
Carson Jarvis (@CarsonJarvisAI)
github
https://github.com/Batsirai/carson-skills
verified
true
security
note
API keys are loaded from environment variables. No credentials are embedded in the skill or scripts.

AEO (Answer Engine Optimization) System

Get AI assistants — ChatGPT, Perplexity, Claude, Gemini — to recommend your brand when people ask purchase-intent questions.

What This Is

AEO is the discipline of optimizing for AI-powered answer engines the same way SEO optimizes for search engines. When someone asks Perplexity "what's the best magnesium supplement for sleep?" — AEO determines whether your brand gets named.

This skill gives an OpenClaw agent the ability to:

  1. Audit a brand's current AEO infrastructure across all 7 layers
  2. Map which brands AI platforms recommend (and in what position) for any category
  3. Track position changes week over week
  4. Build the missing infrastructure (Answer Hub, brand-facts.json, schema, citations)
  5. Maintain the system with a weekly 90-minute protocol

When to Load This Skill

  • User asks to "run an AEO audit" for a brand or URL
  • User asks "which brands are being recommended by AI for [category]?"
  • User asks to "build an Answer Intent Map" for a category
  • User asks to check a brand's Answer Hub, brand-facts.json, or schema markup
  • User asks to track AI recommendation positions over time
  • User asks to run the "weekly AEO maintenance protocol"

The 7-Layer AEO Framework

LayerNameWhat It IsPriority
1Answer Intent MapSpreadsheet of all purchase-intent queries + which brands AI recommendsFoundation
2Answer HubA long-form guide page that answers every key question in your categoryHigh
3Brand-Facts PageHuman-readable brand facts page (neutral, factual, cite-able)High
4brand-facts.jsonMachine-readable brand data at /.well-known/brand-facts.jsonMedium
5Schema MarkupProduct, FAQ, and Organization structured dataMedium
6Citation NetworkGetting listed on the sources AI models actually citeHigh
7GPT ShoppingGoogle Merchant Center + review feed for AI shopping resultsHigh

Prerequisites

Required:

  • PERPLEXITY_API_KEY — enables direct API queries (get free at perplexity.ai/settings/api)
  • Node.js v18+ (for the answer-intent-map.js script)

Optional:

  • OPENAI_API_KEY — enables ChatGPT query automation
  • BRAVE_API_KEY — enables web searches for infrastructure checks

Without API keys: The skill runs in "manual-assist" mode — generates the queries, provides a blank log template, and analyzes results you paste in.


Core Workflows

Workflow 1: Full AEO Audit

Trigger: "Run an AEO audit for [brand URL]"

Steps:

  1. Fetch and analyze the brand's website for AEO infrastructure:

- Check for Answer Hub page (/guides/ or similar long-form page) - Check for Brand-Facts page (/brand-facts) - Check for machine-readable data (/.well-known/brand-facts.json) - Audit schema markup on product pages (via Rich Results API or web_fetch) - Check for a Wikidata entry - Check Google Merchant Center eligibility signals

  1. Score each of the 7 layers (0–3 scale):

- 0 = Doesn't exist - 1 = Exists but incomplete or outdated - 2 = Exists, functional, minor gaps - 3 = Complete, current, optimized

  1. Generate a gap analysis report with:

- Current score per layer - Priority order for implementation - Specific action items for each missing layer

Output: Markdown report saved as aeo-audit-[brand]-[date].md


Workflow 2: Answer Intent Map

Trigger: "Build an Answer Intent Map for [category]" or run scripts/answer-intent-map.js

Steps:

  1. Generate query list from four types:

- Category queries: "best [product] for [use case]" (10–15 queries) - Comparison queries: "[brand] vs [competitor]" (10 queries) - Brand queries: "is [brand] worth it" (5 queries) - Educational queries: "does [ingredient] help with [condition]" (10 queries)

  1. For each query, query available platforms:

- Perplexity API (structured JSON response with citations) - OpenAI API (text response — brand names extracted by parser) - Browser fallback for Claude and Gemini

  1. Parse responses to extract:

- Brand names mentioned (position 1, 2, 3) - Source URLs cited - Key verbatim quotes

  1. Write results to JSON data file + Markdown summary report

Output: answer-intent-map-[category]-[date].json + .md summary

Run the script:

node scripts/answer-intent-map.js \
  --category "magnesium supplements" \
  --brand "MyBrand" \
  --queries 20

# Or with a config file:
node scripts/answer-intent-map.js --config ./aeo-config.json

Workflow 3: Weekly Maintenance Protocol

Trigger: "Run weekly AEO maintenance" or scheduled cron

Steps:

  1. Load the brand's Answer Intent Map (top 15 priority queries)
  2. Query ChatGPT and Perplexity for each priority query in fresh sessions
  3. Compare results against previous week's log (detect position changes)
  4. Generate maintenance report:

- Position changes (up/down/new competitors) - New sources being cited this week - Recommended Answer Hub updates

  1. Check brand-facts.json for stale lastUpdated timestamp
  2. Check Google Merchant Center for disapprovals (via browser if needed)

Output: aeo-weekly-report-[date].md

Use the checklist: templates/weekly-maintenance-checklist.md


Workflow 4: Citation Network Analysis

Trigger: "Analyze AEO citations for [category]"

Steps:

  1. Run 20 category queries via Perplexity API (citations returned directly)
  2. Extract all unique source URLs from responses
  3. Group and count by domain
  4. Identify top 10 most-cited external sources in the category
  5. Generate outreach priority list

Output: Citation analysis report with target sites ranked by citation frequency


Workflow 5: Infrastructure Build

Trigger: "Build AEO infrastructure for [brand]" or "Set up brand-facts.json"

Steps:

  1. Ask for brand details (or load from aeo-config.json)
  2. Generate from templates:

- brand-facts.json → templates/brand-facts.json (fill placeholders) - Answer Hub page → templates/answer-hub-template.md - Schema markup snippet (JSON-LD for product pages)

  1. Provide implementation instructions per asset

Configuration

Create aeo-config.json in your working directory:

{
  "brandName": "Your Brand Name",
  "brandUrl": "https://yourbrand.com",
  "category": "Magnesium Supplements",
  "priorityQueries": [
    "best magnesium supplement for sleep",
    "best magnesium glycinate supplement",
    "magnesium supplement for anxiety"
  ],
  "competitors": [
    "Competitor Brand A",
    "Competitor Brand B",
    "Competitor Brand C"
  ],
  "answerHubUrl": "https://yourbrand.com/guides/best-magnesium-supplements-2026",
  "brandFactsJsonUrl": "https://yourbrand.com/.well-known/brand-facts.json"
}

Output Files

FileDescription
aeo-audit-[brand]-[date].mdInfrastructure audit report
answer-intent-map-[category]-[date].jsonRaw AI query results
answer-intent-map-[category]-[date].mdHuman-readable competitive summary
aeo-weekly-report-[date].mdWeekly maintenance report
citation-analysis-[category]-[date].mdCitation network analysis

Usage Examples

# Full infrastructure audit
"Run an AEO audit for mybrand.com"

# Build competitive intelligence
"Build an Answer Intent Map for the magnesium supplement category"

# Quick position check
"Check if mybrand.com is being recommended by Perplexity for 'best magnesium for sleep'"

# Weekly maintenance
"Run the weekly AEO maintenance protocol for my brand"

# Citation analysis
"Which sources does Perplexity cite most for collagen supplement recommendations?"

# Generate brand-facts.json
"Generate the brand-facts.json template for [brand details]"

# Scoring review
"Score my AEO infrastructure for mybrand.com on all 7 layers"

Platform Limitations

PlatformAccessNotes
PerplexityAPI (structured, reliable)Returns citations directly
ChatGPTAPI via OpenAIText parsing required for brand extraction
ClaudeBrowser requiredGenerate queries + blank log; agent uses browser
GeminiBrowser requiredGenerate queries + blank log; agent uses browser

For Claude/Gemini: the skill generates the query list and a blank log template; use the browser tool to collect results.

Rate limits: Perplexity free tier ≈ 20 requests/minute. For 50+ queries, add --delay 3000 to the script.


File Structure

aeo-system/
├── SKILL.md                              ← This file
├── README.md                             ← Human-readable overview
├── scripts/
│   └── answer-intent-map.js              ← Core query automation script
└── templates/
    ├── answer-hub-template.md            ← Answer Hub page template
    ├── brand-facts.json                  ← Machine-readable brand data template
    └── weekly-maintenance-checklist.md  ← 90-minute weekly protocol

*AEO System v1.0 — February 2026* *A product by Carson Jarvis (@CarsonJarvisAI)*

平台分布

OpenClaw

97.6%
按下载量换算4,270

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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