SEO Content V2

Content Quality & E-E-A-T Analysis

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/seo-content from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Content Quality & E-E-A-T Analysis

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: E-E-A-T Framework (updated Sept 2025 QRG), Content Metrics, AI Content Assessment (Sept 2025 QRG addition), AI Citation Readiness (GEO signals), Content Freshness, Output.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when auditing content quality, readability, thin content risk, or E-E-A-T signals.
• Use when the user wants a content-focused SEO review rather than a full technical audit.
• Use when checking whether content is structured and trustworthy enough for search and AI citation.
• Use when the request clearly matches the imported source intent: >.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

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

Read seo/references/eeat-framework.md for full criteria.

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)

Examples

Example 1: Ask for the upstream workflow directly

Use @seo-content-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @seo-content-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @seo-content-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @seo-content-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/seo-content, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Content Metrics

Word Count Analysis

Compare against page type minimums:

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)

Imported: AI Content Assessment (Sept 2025 QRG addition)

Google's raters now formally assess whether content appears AI-generated.

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.

Imported: 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

AI Search Visibility & GEO (2025-2026)

Google AI Mode launched publicly in May 2025 as a separate tab in Google Search, available in 180+ countries. Unlike AI Overviews (which appear above organic results), AI Mode provides a fully conversational search experience with zero organic blue links, making AI citation the only visibility mechanism.

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, FAQ (for non-Google AI platforms), and structured content schemas help AI systems parse and attribute content
• 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): GEO is the emerging discipline of optimizing content specifically for AI-generated answers. Key GEO signals include: quotability (clear, concise extractable facts), attribution (source citations within your content), structure (well-organized heading hierarchy), and freshness (regularly updated data). Cross-reference the seo-geo skill for detailed GEO workflows.

Imported: 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

Imported: Output

Content Quality Score: XX/100

E-E-A-T Breakdown

AI Citation Readiness: XX/100

Issues Found

Recommendations

Imported: 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.

Imported: Error Handling

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.