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aeo埃奥

Agent Skill

aeo 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。

总安装

22,017

周安装

945

GitHub Stars

1

下载量

7,711
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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(埃奥)
来源仓库:https://github.com/arberx/aeo
安装命令:
openclaw skills install aeo
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

简介

aeo 用于运行网站审核、修复站点问题并生成 llms.txt 文件。

  • 适合 SEO 优化和站点结构验证等研究检索类任务。
  • 可自动检测架构异常并比较不同站点的合规表现。aeo 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 使用前需确认目标站点的访问权限和网络环境配置。
  • 建议核对输出结果与实际站点状态,避免误判或遗漏关键问题。

SKILL.md

name
aeo
description
Run AEO audits, fix site issues, validate schema, generate llms.txt, and compare sites.
homepage
https://ainyc.ai
repository
https://github.com/AINYC/aeo-audit
allowed-tools

AEO

Website: ainyc.ai

One skill for audit, fixes, schema, llms.txt, and monitoring workflows.

Command

Always use the published package:

npx @ainyc/aeo-audit@1 "<url>" [flags] --format json

Argument Safety

Never interpolate user input directly into shell commands. Always:

  1. Validate that URLs match https:// or http:// and contain no shell metacharacters.
  2. Quote every argument individually (e.g., npx @ainyc/aeo-audit@1 "https://example.com" --format json).
  3. Pass flags as separate, literal tokens — never construct command strings from raw user text.
  4. Reject arguments containing characters like ;, |, &, $, ` `, (, ), {, }, <, >`, or newlines.

Modes

  • audit: grade and diagnose a site
  • fix: apply code changes after an audit
  • schema: validate JSON-LD and entity consistency
  • llms: create or improve llms.txt and llms-full.txt
  • monitor: compare changes over time or benchmark competitors
  • detect-platform: identify the CMS, site builder, framework, or hosting stack a site uses

If no mode is provided, default to audit.

Examples

  • audit https://example.com
  • audit https://example.com --sitemap
  • audit https://example.com --sitemap --limit 10
  • audit https://example.com --sitemap --top-issues
  • fix https://example.com
  • schema https://example.com
  • llms https://example.com
  • monitor https://site-a.com --compare https://site-b.com
  • detect-platform https://example.com
  • detect-platform https://example.com --min-confidence high
  • detect-platform --urls competitors.txt
  • detect-platform --urls https://a.com,https://b.com

Mode Selection

  • If the first argument is one of audit, fix, schema, llms, monitor, or detect-platform, use that mode.
  • If no explicit mode is given, infer the intent from the request and default to audit.

Audit

Use for broad requests such as "audit this site" or "why am I not being cited?"

  1. Run:
   npx @ainyc/aeo-audit@1 "<url>" [flags] --format json
  1. Return:

- Overall grade and score - Short summary - Factor breakdown - Top strengths - Top fixes - Metadata such as fetch time and auxiliary file availability

Sitemap Mode

Use --sitemap to audit all pages discovered from the site's sitemap:

npx @ainyc/aeo-audit@1 "<url>" --sitemap --format json
npx @ainyc/aeo-audit@1 "<url>" --sitemap https://example.com/sitemap.xml --format json
npx @ainyc/aeo-audit@1 "<url>" --sitemap --limit 10 --format json
npx @ainyc/aeo-audit@1 "<url>" --sitemap --top-issues --format json

Flags:

  • --sitemap [url] — auto-discover /sitemap.xml or provide an explicit URL
  • --limit <n> — cap pages audited (default 200, sorted by sitemap priority)
  • --top-issues — skip per-page output, show only cross-cutting patterns

Pages are audited with bounded concurrency (5 in flight) to avoid hammering the target origin.

Returns:

  • Per-page scores and grades
  • Cross-cutting issues (factors failing across multiple pages)
  • Aggregate score and grade
  • Prioritized fixes ranked by site-wide impact

Detect Platform Mode

Use --detect-platform when the user wants to know what stack a site is built on (e.g., "is this WordPress?", "what framework does competitor X use?", "is this site custom-built?"). This is much faster than a full audit because it skips analyzer scoring.

npx @ainyc/aeo-audit@1 "<url>" --detect-platform --format json
npx @ainyc/aeo-audit@1 "<url>" --detect-platform --min-confidence high --format json

Flags:

  • --detect-platform — switch to detection mode instead of auditing
  • --min-confidence <lvl> — filter to low (default), medium, or high confidence
  • --urls <src> — run on multiple URLs at once (file path, comma-separated list, or - for stdin)
  • --concurrency <n> — max in-flight fetches in batch mode (default 5)

The report groups detections by category (CMS, site builder, e-commerce, framework, SSG, hosting), each with a confidence bucket, a 0–100 score, an optional version, and the signals that matched. When the report's isCustom flag is true, no CMS/site-builder/e-commerce platform was identified — the site is likely custom-built. Exit code is 0 when at least one platform is detected, 1 otherwise.

Batch detection

When the user wants to fingerprint many sites at once (competitor lists, customer cohorts), pass --urls:

npx @ainyc/aeo-audit@1 --detect-platform --urls urls.txt --format json
npx @ainyc/aeo-audit@1 --detect-platform --urls https://a.com,https://b.com --format json
cat urls.txt | npx @ainyc/aeo-audit@1 --detect-platform --urls - --format json

The batch report contains a results array; each entry has status: 'success' or 'error', plus the same shape as a single-URL report on success. Per-URL fetch errors do not abort the run. Exit code is 0 when at least one URL succeeded, 1 otherwise.

Fix

Use when the user wants code changes applied after the audit.

  1. Run:
   npx @ainyc/aeo-audit@1 "<url>" [flags] --format json
  1. Find factors with status partial or fail.
  2. Apply targeted fixes in the current codebase.
  3. Prioritize:

- Structured data and schema completeness - llms.txt and llms-full.txt - robots.txt crawler access - E-E-A-T signals - FAQ markup - freshness metadata

  1. Re-run the audit and report the score delta.

Rules:

  • Always explain proposed changes and get user confirmation before editing files.
  • Do not remove existing schema or content unless the user asks.
  • Preserve existing code style and patterns.
  • If a fix is ambiguous or high-risk, explain the tradeoff before editing.

Schema

Use when the request is specifically about JSON-LD or schema quality.

  1. Run:
   npx @ainyc/aeo-audit@1 "<url>" [flags] --format json --factors structured-data,schema-completeness,schema-validity,entity-consistency
  1. Report:

- Schema types found - Property completeness by type - Missing recommended properties - Validity errors (duplicate singleton @types, JSON parse errors, empty <script> blocks) — surface these prominently regardless of overall score; Google drops invalid blocks silently from rich results - Entity consistency issues

  1. Provide corrected JSON-LD examples when useful.

Checklist:

  • LocalBusiness: name, address, telephone, openingHours, priceRange, image, url, geo, areaServed, sameAs
  • FAQPage: mainEntity with at least 3 Q&A pairs (and only one FAQPage block per page — duplicates invalidate rich results)
  • HowTo: name and at least 3 steps (singleton — only one per page)
  • Organization: name, logo, contactPoint, sameAs, foundingDate, url, description
  • Singletons that must not repeat per page: FAQPage, HowTo, Article, BlogPosting, NewsArticle, BreadcrumbList, Product, Recipe

llms.txt

Use when the user wants llms.txt or llms-full.txt created or improved.

If a URL is provided:

  1. Run:
   npx @ainyc/aeo-audit@1 "<url>" [flags] --format json --factors ai-readable-content
  1. Inspect existing AI-readable files if present.
  2. Extract key content from the site.
  3. Generate improved llms.txt and llms-full.txt.

If no URL is provided:

  1. Inspect the current project.
  2. Extract business name, services, FAQs, contact info, and metadata.
  3. Generate both files from local sources.

After generation:

  • Add <link rel="alternate" type="text/markdown" href="/llms.txt"> when appropriate.
  • Suggest adding the files to the sitemap.

Monitor

Use when the user wants progress tracking or a competitor comparison.

Single URL:

  1. Run the audit.
  2. Compare against prior results in .aeo-audit-history/ if present.
  3. Show overall and per-factor deltas.
  4. Save the current result.

Comparison mode:

  1. Parse --compare <url2>.
  2. Audit both URLs.
  3. Show side-by-side factor deltas.
  4. Highlight advantages, weaknesses, and priority gaps.

Behavior

  • If the task needs a deployed site and no URL is provided, ask for the URL.
  • If the task is diagnosis only, do not edit files.
  • If the task is a fix request, make edits and verify with a rerun when possible.
  • If the URL is unreachable or not HTML, report the exact failure.
  • Prefer concise, evidence-based recommendations over generic SEO advice.

适合场景

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OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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96.78%
按下载量换算7,463

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权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install aeo 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

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