GEO Content Quality & E-E-A-T Assessment
Scores web content on E-E-A-T and AI citability, producing a structured GEO-CONTENT-ANALYSIS.md report.
Read-mostly assessment skill (Read/Grep/Glob/WebFetch/Write) with no destructive defaults, credential handling, or covert exfiltration; no red-line risk found. However allowed-tools include Bash and Write without least-privilege scoping, user confirmation, rollback guidance, or full data-flow disclosure; 13 deducted.
Scoring method (E-E-A-T 25 points each, tabulated signals) is internally consistent and the output template is explicit; but no test coverage for this skill's key paths (repo tests only cover fetch_page.py), no abnormal-input handling, and several required signals (inbound citations, Wikipedia presence) are not obtainable via Read/Grep, leaving executability questionable; 11 deducted.
Scenario and I/O are clear (content quality and AI citability), with concrete word-count and structure benchmarks; but no declared non-fit boundaries (private pages, non-HTML), no Chinese-language support, and no statement of assessment accuracy limits; 7 deducted.
Well-structured SKILL.md with version, frontmatter, tags, and progressive disclosure; MIT license is explicit; but no changelog, no known-limitations disclosure, and unclear maintenance responsibility or update path; 6 deducted.
The methodology is more structured than manual review and the output template is directly usable; but many signals (authoritative backlinks, media citations, HTTPS validity, verified reviews) exceed what static reads can verify, so scores risk being shallow heuristics with unproven marginal value over alternatives; 9 deducted.
Key factual claims (e.g., the December 2025 Google QRG update applying to all competitive queries) carry no citations, market statistics are unsourced, and there is no test or third-party execution evidence for this skill path — only author-authored methodology; 7 deducted.
- Many E-E-A-T signals (backlinks, Wikipedia presence, HTTPS validity, review authenticity) cannot be verified via static reads, so scores may be shallow and partially irreproducible.
- Cited Google guideline updates and market statistics lack sources; verify independently before relying on them.
- allowed-tools include Bash and Write; monitor execution scope and file writes in practice.
- No Chinese-language support or guidance for non-English sites; effectiveness for Chinese users is unknown.
- Publisher is unverified, with no changelog or maintenance commitment; long-term availability is uncertain.
What does this skill do, and when should you use it?
This is one sub-skill of the geo-seo-claude repository (a 16-skill collection), located at skills/geo-content/SKILL.md, focused on content quality and E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) evaluation. It builds on the premise that Google's December 2025 Quality Rater Guidelines update extends E-E-A-T to all competitive queries, and assesses whether content deserves to be cited by AI search platforms. The skill defines a 100-point scoring system (25 points per dimension) plus a -5 to +10 topical authority modifier, and checks word-count floors, readability (Flesch 60-70), paragraph/heading structure, internal linking, freshness, and low-quality AI content patterns. Output is a GEO-CONTENT-ANALYSIS.md report with per-dimension scores, page-level metrics, most-citable passages, and prioritized recommendations.
Fetches target pages (homepage, blog posts, service/product pages); evaluates E-E-A-T signals per dimension at 25 points each (first-person accounts, original data, author credentials, external citations, contact info, HTTPS, disclosures); assesses content quality metrics (minimum word counts per page type, sentence/paragraph length, passive voice, heading hierarchy); flags low-quality AI content patterns (generic phrasing, no original insight, filler); scores freshness by update recency (excellent within 3 months, critical at 24+ months); assesses topical authority (page count and clustering, -5 to +10 modifier); and generates GEO-CONTENT-ANALYSIS.md containing a 0-100 overall score, per-page citability ratings, most/least citable passages, and categorized recommendations (quick wins, content gaps, E-E-A-T improvements).
- A content marketer checking a blog post for E-E-A-T gaps (missing author bio, no original data) before publishing, to improve odds of AI citation
- An SEO consultant delivering an AI-search visibility audit to a client, using per-page citability ratings and passage analysis as deliverables
- A site owner identifying trust-signal gaps (no privacy policy, no contact info, undisclosed affiliate links) and fixing them via the quick-wins list
- A content team evaluating topical authority to find uncovered subtopics and plan a topic-cluster structure
- An editorial team auditing content freshness and prioritizing updates to high-value pages untouched for 12+ months
What are this skill's strengths and limitations?
- Fully transparent scoring: every E-E-A-T signal has explicit point values and scoring rules, making results reproducible and explainable
- Structured Markdown output with per-page word count, readability, heading structure, and citability ratings — suitable to hand directly to clients
- Aligned with the December 2025 Quality Rater Guidelines change extending E-E-A-T to all competitive queries; targets AI citation rather than traditional rankings
- Distinguishes low-quality AI content patterns from high-quality signals, consistent with Google's March 2024 stance on AI-generated content
- MIT licensed, free, and part of a 16-skill collection usable alongside /geo audit and other commands
- Many signals (inbound citations, media mentions, Wikipedia presence) require live web verification; scoring quality depends on the executing model's judgment with no automated test suite guaranteeing consistency
- Word-count and readability benchmarks are heuristic floors; the skill itself notes they are not direct ranking factors and efficacy is unverified by third parties
- Topical authority scoring relies on estimating whole-site coverage from fetched pages, which can under- or over-count
- The skill only analyzes and reports; it does not rewrite content — fixes require separate execution
- README marketing metrics ($850M market, +527% AI traffic growth, etc.) are cited without sources
How do you install this skill?
This skill ships inside the zubair-trabzada/geo-seo-claude collection. Recommended one-command install: curl -fsSL https://raw.githubusercontent.com/zubair-trabzada/geo-seo-claude/main/install.sh | bash (use install-win.sh from Git Bash on Windows). Requires Python 3.8+, Claude Code CLI, and Git; dependencies go into an isolated venv at ~/.claude/skills/geo/.venv/. Alternatively, git clone the repo and run ./install.sh. Standalone manual installation of just this sub-skill is not documented; cloning the repo and referencing the skills/geo-content/ directory is the direct route.
How do you use this skill?
After installation, run /geo content <url> in Claude Code to assess a site. Flow: fetch homepage and key pages → score four E-E-A-T dimensions (25 points each) → check content quality and AI content signals → assess topical authority → generate GEO-CONTENT-ANALYSIS.md. Score interpretation: 85-100 exceptional AI citation candidate; 55-69 average with multiple gaps; 0-39 needs a fundamental content strategy overhaul.
How does this skill compare with similar options?
The source names no specific competitor products, so no comparison can be made. Its differentiation from generic SEO tooling — focus on AI citability over keyword rankings — is positional, not a named comparison.