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Agent Skill · 研究检索

aeo-analytics-free(免费 AEO 分析)

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

aeo-analytics-free 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 4,270。

总安装 34,072周安装 1,434GitHub Stars 公开资料未说明下载 11,931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-analytics-free(免费 AEO 分析)
来源仓库:https://github.com/psyduckler/aeo-analytics-free
安装命令:
openclaw skills install aeo-analytics-free
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-analytics-free

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-analytics-free 跟踪品牌在 AI 助手中的可见性与引用情况。适用于品牌曝光度分析与 AEO(答案引擎优化)策略制定。仅使用免费工具运行扫描,输出结果供人工解读与决策参考。注意区分样本数据与真实趋势,避免误判整体表现。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

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-analytics-free;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-analytics-free 怎么安装?

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

aeo-analytics-free 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-analytics-free 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-analytics-free 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-analytics-free 用的人多吗?

公开数据是累计安装 34,072 次,近一周安装 1,434 次,下载 11,931 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-analytics-free 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-analytics-free

免费 AEO 分析 · 本页

34,072—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-analytics-free
description
>

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/<domain>.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

  1. 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/<domain>.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

平台分布

OpenClaw

90.58%
按下载量换算10,807

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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