---

description: Analyze content for search engine and AI discoverability. Use when writing new blog posts, documentation, or what-is pages, or when reviewing existing content for SEO and AEO (evaluates titles, meta descriptions, headings, and AEO patterns).

/seo-analyze Command

Use this when: You're creating new content (blog post, documentation) or want to review existing content for visibility in traditional search (SEO) and AI answer engines (AEO/GEO).

Usage

/seo-analyze <file-path>

Examples:

• /seo-analyze content/blog/2026/01/my-post/index.md - Analyze a blog post
• /seo-analyze content/docs/esc/get-started/_index.md - Analyze documentation
• /seo-analyze content/what-is/infrastructure-as-code.md - Analyze a what-is page

Instructions for Claude

Step 1: Detect target file

Determine the target file from (in order of priority):

1. Explicit file path argument
2. Currently open file in IDE context
3. Ask the user which file to analyze

If no file can be determined, use AskUserQuestion:

Which file would you like me to analyze?

Step 2: Analyze current state

Read the target file and extract:

Frontmatter:

• title - Current title tag
• meta_desc - Current meta description
• h1 - First H1 if different from title
• date - Publication date (for blog posts)
• authors - Author information (for blog posts)

Content structure:

• H1 and H2 headings
• Opening paragraph (first ~150 words)
• Presence of lists, tables, FAQ sections
• Internal links (count and anchor text quality)
• Word count and content depth

Step 3: Evaluate AEO patterns

Load reference: Read references/aeo-checklist.md

Scan content against AI discoverability patterns. For each pattern, note what you observe -- not a binary verdict. Good content may not exhibit every pattern, and that's fine.

| Pattern | What to Look For | |---------|------------------| | Quotable Definition | Opening paragraph contains a clear, direct answer | | Semantic Chunking | Each section covers one focused concept | | Citable Claims | Factual statements include specific data | | Comparison Tables | Structured tables for comparisons | | Question Coverage | Content answers what/how/why/when | | Listicle Format | Uses numbered/bulleted lists for key points | | E-E-A-T Signals | Shows experience, expertise, authority, trust | | Down-Funnel Specificity | Addresses specific use cases, integrations, edge cases | | Agent-Friendly Content | Clear CTAs, numbered steps, executable actions |

Step 4: Present analysis and recommendations

Present findings in a structured format:

---

AEO Analysis: [filename]

Summary

| Category | Score | Key Observations | |----------|-------|------------------| | AEO Readiness | X/10 | [observations] |

Observations

For each AEO pattern, describe what you found. Focus on the patterns most relevant to this content type (see the Content Type Applicability table in references/aeo-checklist.md).

Recommendations

Present educational suggestions the writer can consider. Focus on why each suggestion matters for search and AI discoverability, not just what to change.

1. [Observation]

• Why it matters: [How this affects search/AI visibility]
• Suggestion: [What the writer could consider]

2. [Observation]

• Why it matters: [How this affects search/AI visibility]
• Suggestion: [What the writer could consider]

Note: These are suggestions to consider, not mandatory changes. Good content may not check every box -- the goal is awareness of these patterns, not rigid compliance.

---

Content type-specific guidance

Blog posts

Additional considerations for blog content:

• Recency signal: Year in title or prominent placement
• Author credibility: Author has bio with relevant expertise
• Social meta: og:description present and not a placeholder. The og:image is generated on-brand at build time from the title + feature_image — verify feature.png is a real image, not the unmodified placeholder
• Call to action: Clear next step for readers

Documentation

Additional considerations for docs:

• Task orientation: Helps users accomplish specific goals
• Code examples: If the content includes code, verify it appears functional and copy-pasteable
• Prerequisites: Clear about what users need to know/have
• Next steps: Links to related content

What-is pages

Additional considerations for explainer content:

• Definition first: Clear definition in first paragraph
• Comparison context: How it relates to alternatives
• Use cases: When and why to use this
• Getting started: Path to hands-on experience

Example output

## AEO Analysis: `content/blog/2026/01/config-management/index.md`

### Summary

| Category | Score | Key Observations |
|----------|-------|------------------|
| AEO Readiness | 6/10 | Strong structure, missing quotable definition and E-E-A-T signals |

### Observations

- **Quotable Definition**: The opening paragraph introduces the topic but doesn't provide a standalone definition an AI tool could extract.
- **Semantic Chunking**: Good -- each H2 covers a distinct concept.
- **FAQ Sections**: Not present. Could be valuable given the topic has common evaluation questions.
- **Citable Claims**: Two specific data points found. Could benefit from more.
- **E-E-A-T Signals**: No author credentials or real-world usage examples visible.
- **Down-Funnel Specificity**: Covers general concepts well but doesn't address integration scenarios.

### Recommendations

**1. Opening paragraph lacks a quotable definition**
- **Why it matters**: AI answer engines extract opening paragraphs to answer "what is" queries. Without a clear, standalone definition, the content is less likely to be cited.
- **Suggestion**: Consider leading with a one-sentence definition of configuration management that could stand on its own if quoted.

**2. No FAQ section for common evaluation questions**
- **Why it matters**: Q&A format directly matches how users query AI tools. For a topic like configuration management, readers often have specific "does it work with X?" questions.
- **Suggestion**: Consider adding a FAQ section that addresses integration questions, migration concerns, or comparison criteria relevant to the topic.

**3. Limited E-E-A-T signals**
- **Why it matters**: Both Google and AI systems prefer citing sources that demonstrate real-world expertise. Author credentials and production experience increase citation likelihood.
- **Suggestion**: Consider adding author context or referencing real deployment scenarios where relevant.

Integration notes

This skill is recommended as a follow-up to:

• /new-blog-post - After creating a new blog post
• /glow-up - After comprehensive content review
• Manual content creation - Before committing new documentation