Skills

SERP & Content Gap Analysis Skill

Purpose

This Skill transforms search performance data and SERP observations into:

• Clear summaries of the current landscape.
• Prioritised content opportunities (new pages, rewrites, expansions).
• Inputs for AEO/snippet briefs and topic cluster planning.

It operationalises the data-first principles defined in seo-os.SKILL.md.

When to Use

Call this Skill when the user:

• Provides exports from:
  • Google Search Console, Ahrefs, Semrush, or similar tools.
  • Consolidated Markdown summaries from gsc_ahrefs_clean.py.
• Asks for:
  • Content gap analysis.
  • Striking-distance keyword opportunities.
  • SERP/competitor benchmarking.
  • Help prioritising which pages or topics to work on next.

If no data is provided, this Skill can still:

• Work from small manually-supplied lists of queries or URLs.
• Fall back to pattern-based reasoning, clearly marked as inference.

Expected Inputs

Any combination of:

• CSV exports from GSC/Ahrefs/Semrush (ideally pre-processed by gsc_ahrefs_clean.py into Markdown).
• Raw or summarised tables including, where possible:
  • query
  • page / url
  • clicks
  • impressions
  • position
• Free-text notes from the user describing:
  • Priority sections of the site.
  • Business goals and constraints.
  • Key competitors and target queries.

Typical Outputs

• Dataset summary: scope, time range, metrics available.
• By-page performance overview.
• Striking-distance query lists.
• Grouped question families by intent (definition, how-to, comparison, etc.).
• A prioritised opportunity list:
  • New pages to create.
  • Existing pages to expand or refocus.
  • FAQs and sections to add.
• Clear pointers for:
  • aeo-snippet-writer.SKILL.md (content to draft).
  • Briefs using templates/aeo-brief.md.

Core Behaviours

1. Summarise the Dataset

Start by summarising the scope and structure of the data:

### Dataset Summary
- Data source: [GSC / Ahrefs / Semrush / mixed]
- Time range: [dates]
- Scope: [full site / specific section / locale / device]
- Metrics included: [queries, pages, clicks, impressions, positions, etc.]

If the data came from gsc_ahrefs_clean.py, restate its key tables:

• Overall row, unique query, and unique page counts.
• Top-performing pages by clicks/impressions.
• Striking-distance queries (positions ~6–20).

2. Apply Clear Prioritisation Rules

Explicitly state which rules you are applying:

• Striking Distance (Positions ~6–20)

  • Candidates for snippet-focused rewrites and minor expansions.
  • Target: move to top 5 and/or win featured snippets.

• High Impressions + Low CTR

  • Good snippet and title/description optimisation candidates.
  • Fix by improving intro answer blocks, meta content, and page structure.

• Multiple Queries → Single URL

  • Pages where additional sections/FAQs can capture more intents.
  • Potential to serve as pillar or hub content.

• Queries With No Relevant URL

  • Clear content gaps requiring new pages or major subsections.

Always link these rules back to visible data: specify which queries, pages, and metrics support each finding.

3. Group by Question Family and Intent

Organise opportunities by question/intent family:

• Definition / “What is…”
• How-to / Process
• Tools / Templates / Resources
• Costs / Time / Difficulty
• Comparisons / “X vs Y”
• Examples / Use Cases

For each family, note:

• Which existing pages partially cover it.
• Where net-new content is needed.
• Which opportunities are suitable for:
  • Short snippet answers.
  • Long-form pillar content.

4. Output Prioritised Recommendations

Use a consistent structure:

### Content Gap Recommendations

#### High Priority (Quick Wins)
1. **[Existing page URL or new page title]**
   - Opportunity type: [Striking distance / High impressions–low CTR / etc.]
   - Data support: [Key queries, positions, CTR or impression data]
   - Recommended action: [Rewrite intro / Add FAQ section / Create new page]
   - Link to brief: [Create AEO brief using templates/aeo-brief.md]

#### Medium Priority
[Same format]

#### Low Priority (Longer-Term Plays)
[Same format]

#### Inference-Only Ideas (No Direct Data Support)
[Clearly separate speculative opportunities]

Be transparent:

• Everything tied directly to the dataset should be labelled as Data-backed.
• Speculative ideas should be clearly marked as Inference.

5. Competitor SERP Analysis (Optional)

When the user asks for competitor/SERP benchmarking:

• Build a SERP feature inventory:
  • Featured snippet type.
  • PAA questions.
  • Video, image, local, and other modules.
• Analyse the top 3–5 results:
  • URL, content type, and angle.
  • Strengths, weaknesses, and gaps.
• Identify content and format gaps:
  • Missing formats (e.g. no comparison table).
  • Absent or weak FAQs.
  • Under-served question families.

Tie these findings back into the opportunity list, with clear instructions for the AEO writer and GEO Skills.

Dependencies & Supporting Tools

This Skill assumes access to:

• tools/gsc_ahrefs_clean.py
  • Used to convert messy CSVs into Markdown summaries.
  • Produces:
    • A dataset summary.
    • By-page aggregate table.
    • Striking-distance queries table.
• templates/aeo-brief.md
  • Used to capture brief-level details for each high-priority opportunity.

When giving instructions to a human operator:

• Suggest running gsc_ahrefs_clean.py on large exports before pasting data into an AI session.
• Encourage using the AEO brief template for each major recommendation cluster.

Hand-offs to Other Skills

• To AEO + Snippet Writer:

  • Provide a filtered, prioritised list of:
    • Pages to rewrite or expand.
    • New pages to create.
    • Question families and snippet formats to target.
  • For each item, include:
    • Core question(s).
    • Desired snippet format.
    • Key data points and constraints.

• To GEO & AI Visibility:

  • Flag opportunities where:
    • Conversational decision queries are prominent.
    • Comparison or diagnostic content could be highly citeable.
  • Recommend which pages should be treated as GEO pillars.

• To Technical SEO Audit:

  • Surface potential technical issues visible indirectly via the data (e.g. pages with impressions but zero clicks despite strong intent coverage).
  • Suggest follow-up checks (indexation, CWV, internal links).