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Emergence SEO GEO

定义 GEO/SEO 审计方法:用 geo_audit.py 对目标域名做爬取检查,按 Agent-First/Human-First 两套权重对技术可访问性、结构化数据、内容可提取性、答案密度、引用数据、EEAT、站外足迹、机器可读发现 8 项指标评分,并生成含执行摘要、竞品引用差距与 30-60-90 天优化路线图的报告。

emergencescience
emergencescience
热度 34.72
最近更新 2026-06-06 12:25:46
分类:营销依赖:代码执行 / 第三方 API应用场景:市场分析 / 报告撰写 / 研究

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add emergence-seo-geo
Metadata
slug
emergence-seo-geo
title
Emergence SEO GEO: Generative Engine Optimization Auditor
description
Professional GEO/SEO website auditor skill evaluating LLM citation readiness, technical accessibility, and semantic authority.
version
1.1.0
homepage
https://emergence.science/skills/emergence-seo-geo

GEO/SEO Analysis & Audit Skill

This skill defines the technical methodology and analysis workflow for auditing websites against Generative Engine Optimization (GEO) and AI-native search engine (ChatGPT, Perplexity, Gemini, Claude, Copilot, Google AI Overviews) ranking behaviors.


🎯 Core Objective

Audit any target domain's visibility, technical crawlability, and semantic authority under conversational AI models, producing a standardized, high-value assessment report with actionable optimization roadmaps tailored to the type of web application (Agent-First vs. Human-First).


📊 Differentiated Scoring Scorecard (100 Points Total)

To cater to the distinct traversal paths of autonomous agents versus human-oriented conversational search, the audit uses a tailored metric weighting scheme:

Metric Max Points Agent-First Heuristic Human-First Heuristic
1. Technical Accessibility 15 pts - AI crawlers (GPTBot, ClaudeBot, PerplexityBot, etc.) permitted in robots.txt. - Standard search engine crawlers (Googlebot, Bingbot, etc.) permitted in robots.txt.
2. Structured Schema Quality 10 pts - Validity of JSON-LD schemas mapped to agent capabilities or software specs. - Presence and validity of schemas like Product, FAQPage, Organization, HowTo.
3. Content Extractability 15 pts - Section headings, markdown structures, bullet points, and data grids. - Structured comparison layout, QA headers, H1/H2/H3 hierarchies.
4. Answer Density (BLUF) 10 pts - Concise summary blocks for machine ingestion. - High-density 40-70 word answer blocks ("Bottom Line Up Front" formatting).
5. Citations & Empirical Data 15 pts - Hard data points, numeric parameters, and outbound documentation references. - Specific numbers, percentages, trust factors, and reliable source linking.
6. Expertise & Attribution (EEAT) 10 pts - Developer/Publisher credentials, software repositories, and compliance/trust signals. - Verified author credentials, profile bylines, customer review indicators.
7. Off-Page Entity Footprint 15 pts - Hyperlinked references in code repositories, developer registries, or package indexes. - External citations on mainstream wikis, reddit homebrews, forum indices.
8. Machine-Readable Discovery 10 pts - Presence and compliance of /llms.txt and /skill.md at domain root. - Presence of standard /sitemap.xml and OpenGraph/Twitter social meta tags.

🛠️ Step-by-Step Auditing Workflow

Step 1: Local & Automated Crawl Check

Run the automated python script geo_audit.py to calculate the baseline score and scan technical endpoints:

# Audit a B2B SaaS or E-commerce site (Human-first)
python3 geo_audit.py soldy.ai --type human --prompt "AI video generator"

# Audit an agent network app or dev protocol (Agent-first)
python3 geo_audit.py emergence.science --type agent --prompt "openclaw bounty market"

Step 2: E2E Search Engine Indexation Verification (Optional)

To query live index footprints and rank tracking on Tavily, Brave Search, Google, and Bing, append the --e2e flag:

# Requires local CLI tools: `bx` (Brave Search) and `tvly` (Tavily Search)
python3 geo_audit.py emergence.science --type agent --prompt "openclaw bounty market" --e2e

Step 3: Content Optimization Scan

  1. Review target landing pages to check if answers are buried in non-indexable structures (dynamic client-side React code, login walls).
  2. Count word length of key introductory paragraphs to check if they fit the 40-70 word extraction window.

Step 4: Sourcing & EEAT Validation

Verify if claims rely on subjective marketing adjectives instead of verifiable scientific and quantitative facts.


📋 Standard Reporting Structure & Examples

Every generated report must include:

  1. Executive Summary: Core highlights and final benchmark score.
  2. Current State Assessment: Breakdown of the 8 metrics with specific website screenshots or parsed snippets.
  3. Competitor Citation Gap Analysis: Why competitors get cited in ChatGPT/Perplexity for target keywords while the client is omitted.
  4. Actionable Roadmap: Clear 30-60-90 day recommendations (Technical fixes, structural copy rewrite, schema implementation, agentic files integration).

Live Example & Downloadable Report