什么是 AEO Analytics (免费版)？

AEO Analytics (免费版) 是 AEO Skills 套件的重要组成部分，旨在闭环内容创作与效果衡量。通过利用带接地（grounding）功能的 Gemini API 或网页搜索回退机制，该技能允许开发者和营销人员监控其品牌在 AI 生成回答中出现的频率。它同时追踪提及和直接引用，为品牌在不断演变的 AI 助手格局中的权威性提供数据驱动的视图。

利用这些 Openclaw Skills，团队可以检测趋势、识别情感，并实时对比竞争对手的表现。它通过准确展示品牌在 Gemini、ChatGPT 和 Perplexity 等大语言模型眼中的地位，为 AEO 工作提供必要的指标支持。

下载入口:https://github.com/openclaw/skills/tree/main/skills/psyduckler/aeo-analytics-free

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

AEO Analytics (免费版) 应用场景

• 审计 AI 生成回答中的品牌存在感，以了解当前市场份额。
• 追踪回答引擎优化 (AEO) 内容更新随时间推移的有效性。
• 监控竞争对手的可见度，并识别 AI 模型倾向于引用的来源。
• 识别品牌在相关 AI 提示词中缺失的内容缺口。

1. 初始化或加载特定域名的 JSON 数据文件以维持历史扫描记录。
2. 使用启用搜索接地的 Gemini API 执行目标提示词，获取最新的 AI 生成回答和来源链接。
3. 解析响应文本和接地元数据，以检测品牌提及、提取引用 URL 并分类情感。
4. 将结果记录到持久数据文件中，确保所有可见性扫描的历史审计轨迹。
5. 生成综合可见性报告，突出提及率、引用趋势以及内容改进的优先建议。

AEO Analytics (免费版) 配置指南

要开始使用此技能，您需要一个 Gemini API 密钥（可从 Google AI Studio 免费获取）以启用接地功能。如果没有提供密钥，该技能将使用网页搜索作为回退方法。

# 定义您的域名和品牌名称以开始追踪
# 该技能将自动管理 JSON 数据存储
# 路径为 aeo-analytics/.json

AEO Analytics (免费版) 数据架构与分类体系

该技能在结构化的 JSON 文件中组织数据，以确保长期趋势分析。每个文件对应一个特定的域名。

name: aeo-analytics-free
description: >
  Track AI visibility — measure whether a brand is mentioned and cited by AI assistants
  (Gemini, ChatGPT, Perplexity) for target prompts. Runs scans, tracks mention/citation
  rates over time, detects trends, and identifies opportunities. Uses Gemini API free tier
  (with grounding) as primary method, web search as fallback.
  Use when a user wants to: check if AI models mention their brand, track AI citation
  changes over time, measure AEO content effectiveness, monitor competitor AI visibility,
  or audit their brand's presence in AI-generated answers.
  Pairs with aeo-prompt-research-free (identifies prompts) and aeo-content-free
  (creates/refreshes content). This skill closes the loop by measuring results.

AEO Analytics (Free)

Source: github.com/psyduckler/aeo-skills Part of: AEO Skills Suite — Prompt Research → Content → Analytics

Track whether AI assistants mention and cite your brand — and how that changes over time.

Requirements

• Primary: Gemini API key (free from aistudio.google.com) — enables grounding with source data
• Fallback: web_search only — weaker signal but zero API keys needed
• web_fetch — optional, for deeper analysis of cited pages

Input

• Domain (required) — the brand's website (e.g., tabiji.ai)
• Brand names (required) — names to search for in responses (e.g., ["tabiji", "tabiji.ai"])
• Prompts (required for first scan) — list of target prompts to track. Can come from aeo-prompt-research-free output.
• Data file path (optional) — where to store scan history. Default: aeo-analytics/.json

Commands

The skill supports three commands:

scan — Run a new visibility scan

Execute all tracked prompts against the AI model and record results.

report — Generate a visibility report

Analyze accumulated scan data and produce a formatted report.

add-prompts / remove-prompts — Manage tracked prompts

Add or remove prompts from the tracking list.

Scan Workflow

Step 1: Load or Initialize Data

Check if a data file exists for this domain. If yes, load it. If no, create a new one. See references/data-schema.md for the full JSON schema.

Step 2: Run Prompts

For each tracked prompt:

Method A — Gemini API with grounding (preferred): See references/gemini-grounding.md for API details.

1. Send prompt to Gemini API with googleSearch tool enabled

2. From the response, extract:

   • Response text — the AI's answer
   • Grounding chunks — the web sources cited (URLs + titles)
   • Web search queries — what the AI searched for

3. Analyze the response:

   • Mentioned? — Search response text for brand names (case-insensitive, word-boundary match)
   • Mention excerpt — Extract the sentence(s) containing the brand name
   • Cited? — Check if brand's domain appears in any grounding chunk URI
   • Cited URLs — List the specific brand URLs cited
   • Sentiment — Classify the mention context as positive/neutral/negative
   • Competitors — Extract other brand names and domains from response + citations

Method B — Web search fallback (if no Gemini API key):

1. web_search the exact prompt text
2. Check if brand's domain appears in search results
3. Record as "web-proxy" method (less direct than grounding)

Step 3: Save Results

Append the scan results to the data file. Never overwrite previous scans — history is the whole point.

Step 4: Quick Summary

After scanning, output a brief summary:

• Prompts scanned
• Current mention rate and citation rate
• Change vs. last scan (if applicable)
• Any notable changes (new mentions, lost citations)

Report Workflow

Per-Prompt Detail

For each tracked prompt, show:

1. "[prompt text]"
   Scans: [total] (since [first scan date])
   Mentioned: [count]/[total] ([%]) — [trend arrow] [trend description]
   Cited: [count]/[total] ([%])
   Latest: [?/? Mentioned] + [?/? Cited]
   Sentiment: [positive/neutral/negative]
   Competitors mentioned: [list]

If mentioned in latest scan, include the mention excerpt. If not mentioned, note which sources were cited instead and rate the opportunity (HIGH/MEDIUM/LOW).

Summary Section

VISIBILITY SCORE
  Brand mentioned: [X]/[total] prompts ([%]) in latest scan
  Brand cited: [X]/[total] prompts ([%]) in latest scan

TRENDS (last [N] days, [N] scans)
  Mention rate: [%] → [trend]
  Citation rate: [%] → [trend]
  Most improved: [prompt] ([old rate] → [new rate])
  Most volatile: [prompt] (mentioned [X]/[N] scans)
  Consistently absent: [list of prompts never mentioned]

COMPETITOR SHARE OF VOICE
  [Competitor 1] — mentioned in [X]/[total] prompts
  [Competitor 2] — mentioned in [X]/[total] prompts
  [Brand] — mentioned in [X]/[total] prompts

NEXT ACTIONS
  → [Prioritized recommendations based on gaps and trends]

Recommendations Logic

• High opportunity: Prompt has 0% mention rate + no strong owner in citations → create content
• Close to winning: Prompt has mentions but no citations → refresh content for citation-worthiness
• Volatile: Mention rate between 20-60% → content exists but needs strengthening
• Won: Mention rate >80% + citation rate >50% → maintain, monitor for decay

Data Management

• Data file location: aeo-analytics/.json
• Schema: see references/data-schema.md
• Each scan appends to the scans array — never delete history
• Prompts can be added/removed without affecting historical data
• When adding new prompts, they start with 0 scans (no backfill)

Tips

• Run scans at consistent intervals (weekly or biweekly) for meaningful trend data
• After publishing new AEO content, wait 2-4 weeks for indexing before expecting changes
• Gemini's grounding results can vary run-to-run — that's normal. Aggregate data over multiple scans is more reliable than any single result
• Track 10-20 prompts max for a focused view. Too many dilutes the signal
• This skill completes the AEO loop: Research (aeo-prompt-research-free) → Create/Refresh (aeo-content-free) → Measure (this skill) → repeat