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Agent skill

SEO Content Quality Analysis

by AgriciDaniel in AgriciDaniel/claude-seo

Scores page content for E-E-A-T, readability, thinness, and AI-citation readiness, then cleans up AI-typical phrasing in a draft.

MITAuto-check passedMarketing & SEO

Install SEO Content Quality Analysis

skills CLI
$ npx skills add AgriciDaniel/claude-seo --skill seo-content -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install AgriciDaniel/claude-seo seo-content --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/AgriciDaniel/claude-seo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/seo-content .claude/skills/seo-content && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
seo-content
GitHub stars
19k
Token cost
~3.2k tokens
SKILL.md length
1,509 words
Files
2
Skills in repo
33
Repo updated
First seen
Licence
MIT

At a glance

Scores page content for E-E-A-T, readability, thinness, and AI-citation readiness, then cleans up AI-typical phrasing in a draft.

  • Works in 2 steps: Invisible characters… → AI-typical phrasing (changes):…
  • Auditing a page's E-E-A-T signals before or after publishing
  • SKILL.md covers Google's "Who / How / Why"…, E-E-A-T Framework (updated…, Content Metrics and AI Content Assessment (QRG:…, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Google's own who, how, why heuristic from its helpful-content guide opens every audit: who created the content and whether a credentialed byline exists, how it was made including process disclosure for AI-assisted work, and why it exists, to help people rather than to attract clicks. An E-E-A-T scoring pass follows across experience, expertise, authoritativeness, and trustworthiness, backed by reference files for the full framework and its score bands, plus a word-count check against minimums that vary by page type.

When your agent uses it

  • Auditing a page's E-E-A-T signals before or after publishing
  • Checking whether a page is thin content relative to its type
  • Cleaning AI-typical phrasing and watermark characters from a draft

Example prompts

  • “Score this blog post's E-E-A-T against Google's helpful-content heuristic.”
  • “Is this product page too thin compared to its word-count minimum?”
  • “Clean up AI-typical phrasing in this draft before we publish it.”

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Invisible characters (invisible_removed): strips zero-width
  2. AI-typical phrasing (changes): conservative 1:1 swaps from the

What it can do on your machine

Read from SKILL.md and the folder at commit 4b99de2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • developers.google.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

SEO Content Quality Analysis loads about 3.2k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 1,509 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from AgriciDaniel/claude-seo at commit 4b99de2, republished under its MIT licence (© AgriciDaniel). 1,509 words, ~3,179 tokens.

Download SKILL.mdSave it as .claude/skills/seo-content/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
seo-content
description
Evaluate page content for usefulness, E-E-A-T, readability, thinness, and AI citation readiness, plus last-mile draft cleanup (AI-typical phrasing and invisible Unicode watermark characters). Use for content-only analysis, not full-page technical checks.
user-invocable
true
argument-hint
[url]
license
MIT
metadata.author
AgriciDaniel
metadata.version
2.4.2
metadata.category
seo

Content Quality & E-E-A-T Analysis

Google's "Who / How / Why" Test (canonical heuristic)

Before scoring E-E-A-T sub-factors, every page audit should pass Google's own three-question heuristic from the helpful-content guide:

QuestionWhat to look for
Who created it?Visible byline, author bio page, professional credentials. Required where readers expect it; non-negotiable for YMYL.
How was it created?Process disclosure where readers would reasonably ask, especially for AI-assisted content. Original research / first-hand evidence / lived experience.
Why does it exist?"To help people" rather than "to attract search clicks." Watch for niche entry without expertise, content churn for freshness signals, content written to a word-count target.

Primary source: https://developers.google.com/search/docs/fundamentals/creating-helpful-content

When all three answers are weak, the page is at risk under the core ranking system's helpfulness signals (formerly the standalone Helpful Content System, merged into core during the March 2024 update).

E-E-A-T Framework (updated Sept 2025 QRG)

Read ${CLAUDE_PLUGIN_ROOT}/skills/seo/references/eeat-framework.md for full criteria and ${CLAUDE_PLUGIN_ROOT}/skills/seo/references/eeat-scoring-guide.md for score bands.

Experience (first-hand signals)
  • Original research, case studies, before/after results
  • Personal anecdotes, process documentation
  • Unique data, proprietary insights
  • Photos/videos from direct experience
Expertise
  • Author credentials, certifications, bio
  • Professional background relevant to topic
  • Technical depth appropriate for audience
  • Accurate, well-sourced claims
Authoritativeness
  • External citations, backlinks from authoritative sources
  • Brand mentions, industry recognition
  • Published in recognized outlets
  • Cited by other experts
Trustworthiness
  • Contact information, physical address
  • Privacy policy, terms of service
  • Customer testimonials, reviews
  • Date stamps, transparent corrections
  • Secure site (HTTPS)

Content Metrics

Word Count Analysis

Compare against page type minimums:

Page TypeMinimum
Homepage500
Service page800
Blog post1,500
Product page300+ (400+ for complex products)
Location page500-600

Important: These are topical coverage floors, not targets. Google has confirmed word count is NOT a direct ranking factor. The goal is comprehensive topical coverage; a 500-word page that thoroughly answers the query will outrank a 2,000-word page that doesn't. Use these as guidelines for adequate coverage depth, not rigid requirements.

Readability
  • Flesch Reading Ease: target 60-70 for general audience

Note: Flesch Reading Ease is a useful proxy for content accessibility but is NOT a direct Google ranking factor. John Mueller has confirmed Google does not use basic readability scores for ranking. Yoast deprioritized Flesch scores in v19.3. Use readability analysis as a content quality indicator, not as an SEO metric to optimize directly.

  • Grade level: match target audience
  • Sentence length: average 15-20 words
  • Paragraph length: 2-4 sentences
Keyword Optimization
  • Primary keyword in title, H1, first 100 words
  • Natural density (1-3%)
  • Semantic variations present
  • No keyword stuffing
Content Structure
  • Logical heading hierarchy (H1 -> H2 -> H3)
  • Scannable sections with descriptive headings
  • Bullet/numbered lists where appropriate
  • Table of contents for long-form content
Multimedia
  • Relevant images with proper alt text
  • Videos where appropriate
  • Infographics for complex data
  • Charts/graphs for statistics
Internal Linking
  • 3-5 relevant internal links per 1000 words
  • Descriptive anchor text
  • Links to related content
  • No orphan pages
External Linking
  • Cite authoritative sources
  • Open in new tab for user experience
  • Reasonable count (not excessive)

AI Content Assessment (QRG: generative-AI guidance added January 2025; current version September 11, 2025)

Google's raters assess low-quality, scaled, copied, or AI-generated main content patterns rather than AI authorship as a standalone issue.

Acceptable AI Content
  • Demonstrates genuine E-E-A-T
  • Provides unique value
  • Has human oversight and editing
  • Contains original insights
Low-Quality AI Content Markers
  • Generic phrasing, lack of specificity
  • No original insight
  • Repetitive structure across pages
  • No author attribution
  • Factual inaccuracies

Helpful Content System (March 2024): The Helpful Content System was merged into Google's core ranking algorithm during the March 2024 core update. It no longer operates as a standalone classifier. Helpfulness signals are now weighted within every core update. The same principles apply (people-first content, demonstrating E-E-A-T, satisfying user intent), but enforcement is continuous rather than through separate HCU updates. Google now also documents continuous, smaller unannounced core updates between major ones (changelog 2025-12-09).

Gen-AI optimization is SEO (Google docs, published 2026-05-15, last updated 2026-07-10): the official "optimizing for generative AI features" guide states you do not need new AI files, markup, Markdown, content chunking, or AI-specific rewrites; chasing inauthentic "mentions" is unhelpful. AEO/GEO is rebranded SEO rooted in core ranking/quality.

Honest scoping (Google docs, 2026-06-05): per "Using third-party SEO tools, services, and advice," no tool guarantees rankings and third-party tools have no access to Google's internal ranking data. claude-seo's scores are heuristics, not Google-internal signals, so say so in reports, and validate GEO/AEO findings against Google's official guidance (Search Console is the first-party source).

Draft Cleanup: AI Phrasing & Invisible Watermarks

For "humanize this", "remove watermarks", or "clean up this draft", run the bundled cleanup script on the user's own content:

bash
"${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run content_humanize.py draft.md -o cleaned.md
cat draft.md | "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run content_humanize.py --json

Two deterministic passes, both logged in the JSON output:

  1. Invisible characters (invisible_removed): strips zero-width codepoints, directional marks/overrides, Unicode tag characters (hidden text smuggling), and normalizes exotic spaces. Emoji sequences (ZWJ, variation selectors next to emoji) are preserved.
  2. AI-typical phrasing (changes): conservative 1:1 swaps from the replacement table ("delve into" → "explore", etc.). Nothing is paraphrased or added.

Scope honesty: statistical watermarks (SynthID-style token-probability schemes) live in word choice, not codepoints. No tool reliably detects or removes them; do not claim otherwise in reports. This cleanup is for editing the user's own drafts, not for laundering third-party content; decline requests to strip provenance from content the user doesn't own.

AI Citation Readiness (GEO signals)

Optimize for AI search engines (ChatGPT, Perplexity, Google AI Overviews):

  • Clear, quotable statements with statistics/facts
  • Structured data (especially for data points)
  • Strong heading hierarchy (H1->H2->H3 flow)
  • Answer-first formatting for key questions
  • Tables and lists for comparative data
  • Clear attribution and source citations
Show full SKILL.md (597 more words)Show less
AI Search Visibility & GEO (2025-2026)

Google AI Mode is Google's conversational AI search surface. Google upgrades the AI Mode model often (Gemini 3.5 Flash became the default on 2026-05-19, and newer Flash models have shipped since, per blog.google); never tie advice to a model version. Treat third-party AI Mode usage, citation, and link-share figures as methodology-dependent unless primary-sourced, and optimize for both AI Mode and AI Overviews (see the seo-geo skill).

Key optimization strategies for AI citation:

  • Structured answers: Clear question-answer formats, definition patterns, and step-by-step instructions that AI systems can extract and cite
  • First-party data: Original research, statistics, case studies, and unique datasets are highly cited by AI systems
  • Schema markup: Article and other relevant structured content. FAQPage no longer produces Google FAQ rich results; use QAPage only for genuine user Q&A where appropriate
  • Topical authority: AI systems preferentially cite sources that demonstrate deep expertise. Build content clusters, not isolated pages
  • Entity clarity: Ensure brand, authors, and key concepts are clearly defined with structured data (Organization, Person schema)
  • Multi-platform tracking: Monitor visibility across Google AI Overviews, AI Mode, ChatGPT, Perplexity, and Bing Copilot, not just traditional rankings. Treat AI citation as a standalone KPI alongside organic rankings and traffic.

Generative Engine Optimization (GEO): Per Google's AI optimization guide, "optimizing for generative AI search is optimizing for the search experience, and thus still SEO": AI Overviews and AI Mode are grounded in the same ranking and quality systems as classic Search. The optimization signals that matter (quotability, attribution, heading hierarchy, freshness) are SEO fundamentals applied to AI-search surfaces, not a separate discipline. Cross-reference the seo-geo skill for detailed workflows; both surfaces share the primary-source synthesis in ${CLAUDE_PLUGIN_ROOT}/skills/seo-geo/references/google-ai-optimization-guide.md.

Content Freshness

  • Publication date visible
  • Last updated date if content has been revised
  • Flag content older than 12 months without update for fast-changing topics

Google Update Correlation (content and spam updates)

Before attributing a traffic or ranking change to anything, list the confirmed Google updates in that window from the primary-source ledger:

bash
"${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run seo_updates.py --since <yyyy-mm> --json

Every entry cites a Google-owned URL. If freshness.stale is true, say the ledger may miss recent updates and check status.search.google.com before drawing conclusions. A date overlap is a hypothesis, never proof of cause.

Output

Content Quality Score: XX/100
E-E-A-T Breakdown
FactorScoreKey Signals
ExperienceXX/20...
ExpertiseXX/25...
AuthoritativenessXX/25...
TrustworthinessXX/30...

Weights are this skill's own scoring model, ordered to reflect Google's stated hierarchy: Trust is most important (30), then Expertise/ Authoritativeness (25 each), then Experience (20); maxima sum to 100. Google publishes no numeric E-E-A-T weights (only that trust is most important), so treat the split as our internal model. Do not use an equal 25/25/25/25 split (it contradicts Google's "trust is most important").

AI Citation Readiness: XX/100
Issues Found
Recommendations

DataForSEO Integration (Optional)

If DataForSEO MCP tools are available, use kw_data_google_ads_search_volume for real keyword volume data, dataforseo_labs_bulk_keyword_difficulty for difficulty scores, dataforseo_labs_search_intent for intent classification, and content_analysis_summary for content quality analysis.

Error Handling

ScenarioAction
URL unreachable (DNS failure, connection refused)Report the error clearly. Do not guess page content. Suggest the user verify the URL and try again.
Content behind paywall (402/403, login wall)Report that the content is not publicly accessible. Analyze only the visible portion (meta tags, headers) and note the limitation.
Thin content (fewer than 100 words retrievable)Report the findings as-is rather than guessing. Flag the page as potentially JavaScript-rendered or gated, and suggest the user provide the full text directly.

FLOW Framework Integration

For prompt-guided content optimization, use /seo flow optimize <url> and /seo flow win <url>: FLOW's optimize and win prompts provide structured E-E-A-T improvement and BOFU conversion workflows.

© AgriciDaniel, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/seo-content of AgriciDaniel/claude-seo.

  • SKILL.md
  • LICENSE.txt

Open the folder on GitHubat commit 4b99de2

Compare with similar skills

SEO Content Quality Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

SEO Content Quality Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
SEO Content Quality Analysis this skillAgriciDaniel/claude-seo19k—~3.2kAutomated safety check: PassMIT
AI Citability Scorerzubair-trabzada/geo-seo-claude11k2 repos~3.7kAutomated safety check: NotesMIT
GEO Platform Optimizerzubair-trabzada/geo-seo-claude11k2 repos~4.7kAutomated safety check: NotesMIT
SEO Content Auditseranking/seo-skills160—~3kAutomated safety check: PassMIT
SEO ContentAgriciDaniel/codex-seo7975 repos~2.3kAutomated safety check: PassMIT
Blog OutlineAgriciDaniel/claude-blog2.3k1 repos~1.5kAutomated safety check: PassMIT

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Categories

Questions about SEO Content Quality Analysis

What does SEO Content Quality Analysis do?

Scores page content for E-E-A-T, readability, thinness, and AI-citation readiness, then cleans up AI-typical phrasing in a draft. Google's own who, how, why heuristic from its helpful-content guide opens every audit: who created the content and whether a credentialed byline exists, how it was made including process disclosure for AI-assisted work, and why it exists, to help people rather than to attract clicks. An E-E-A-T scoring pass follows across experience, expertise, authoritativeness, and trustworthiness, backed by reference files for the full framework and its score bands, plus a word-count check against minimums that vary by page type.

When should I use SEO Content Quality Analysis?

SEO Content Quality Analysis fits situations like: auditing a page's E-E-A-T signals before or after publishing; checking whether a page is thin content relative to its type; cleaning AI-typical phrasing and watermark characters from a draft.

How do I install SEO Content Quality Analysis in Claude Code?

Run `npx skills add AgriciDaniel/claude-seo --skill seo-content -a claude-code`. Or copy the skill folder (skills/seo-content in AgriciDaniel/claude-seo) into .claude/skills/seo-content in your project. Claude Code loads it when a task matches its description.

How do I install SEO Content Quality Analysis in Codex?

Run `npx skills add AgriciDaniel/claude-seo --skill seo-content -a codex`. Or copy the skill folder (skills/seo-content in AgriciDaniel/claude-seo) into .agents/skills/seo-content in your project. Codex loads it when a task matches its description.

Can I use SEO Content Quality Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AgriciDaniel/claude-seo --skill seo-content -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/seo-content, .gemini/skills/seo-content, .github/skills/seo-content and .opencode/skills/seo-content in your project.

What does SEO Content Quality Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: SEO Content Quality Analysis is instructions for the agent only.

Does SEO Content Quality Analysis access the network?

SKILL.md names 1 domain. As links in the text: developers.google.com. This is read from the text; nothing was executed.

Is SEO Content Quality Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does SEO Content Quality Analysis use?

SEO Content Quality Analysis is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does SEO Content Quality Analysis use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to SEO Content Quality Analysis?

Skills that share tags, products or a category with SEO Content Quality Analysis: AI Citability Scorer (zubair-trabzada/geo-seo-claude, 11k stars), GEO Platform Optimizer (zubair-trabzada/geo-seo-claude, 11k stars), SEO Content Audit (seranking/seo-skills, 160 stars) and SEO Content (AgriciDaniel/codex-seo, 797 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains SEO Content Quality Analysis?

AgriciDaniel (a GitHub user) maintains it in AgriciDaniel/claude-seo, which has 18,574 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 4, 2026.

Source: AgriciDaniel/claude-seo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.