Topical Authority Mapper

Topical Authority Mapper

Most content strategies are just keyword lists turned into blog posts. Real topical authority requires a structured map: pillar pages that own broad topics, cluster pages that go deep on subtopics, and an internal linking architecture that tells Google "we comprehensively cover this subject." This skill builds that map.

Core principle: Google rewards topical depth, not random keyword coverage. A site with 15 interlinked articles that thoroughly cover "sales automation" will outrank a site with 50 unrelated blog posts that happen to mention the phrase. This skill builds the cluster architecture that creates genuine authority.

When to Use

• "What content should we create to dominate [topic]?"
• "Build a topic cluster strategy for our blog"
• "Map out our topical authority for [category]"
• "Create a content calendar based on topic clusters"
• "What content gaps do we have?"

Tool Enhancement (Optional)

Topic cluster mapping is significantly better with keyword data that shows search volume, difficulty, and semantic relationships across hundreds of subtopic variations.

Agent Prompt to User

"I can build a comprehensive topical authority map using competitive analysis and content gap identification. For the most precise results — especially accurate volume data and keyword clustering at scale — I'd recommend connecting a keyword data API."

Recommended: DataForSEO (pay-per-use, ~$0.01/keyword, no monthly minimum)

• Sign up at dataforseo.com → get API login + password
• Set DATAFORSEO_LOGIN and DATAFORSEO_PASSWORD env vars

Alternatives that also work:

• Keywords Everywhere API ($1 per 10 credits = 100K keywords, very cheap) → set KEYWORDS_EVERYWHERE_API_KEY
• SEMrush API (if you have a subscription) → set SEMRUSH_API_KEY
• Ahrefs API (if you have a subscription) → set AHREFS_API_TOKEN

"Want to use one of these, or should I proceed with baseline mode? Baseline uses our existing SEO tools and web research — still produces a strong topic map, but with less granular volume data per subtopic."

Mode Selection

• Enhanced mode — Bulk keyword data via DataForSEO / Keywords Everywhere / SEMrush / Ahrefs. Gets search volume, difficulty, and semantic grouping for every subtopic. Enables data-driven prioritization and precise gap identification. Also supports keyword clustering APIs that automatically group related terms.
• Baseline mode — Uses seo-domain-analyzer for domain metrics, web_search for topic research, reddit-post-finder for question mining, competitor analysis via site-content-catalog. Topic mapping and cluster architecture are equally strong. Volume estimates are directional rather than exact.

Phase 0: Intake

1. Your site URL — For existing content audit
2. Target topics — 1-5 broad topic areas you want authority in (e.g., "sales automation", "content marketing", "data privacy")
3. Competitors — 2-5 competitor URLs who rank well for these topics
4. ICP — Who reads your content? (role, pain, goal)
5. Content capacity — How many articles can your team produce per month?
6. Existing content — Do you have a blog? How many articles? (we'll audit it)
7. Time horizon — 3-month plan? 6-month? 12-month?
8. Tool preference — Enhanced mode with keyword API, or baseline? (see Tool Enhancement above)

Phase 1: Current State Audit

1A: Your Existing Content

Run site-content-catalog on your site:

python3 skills/site-content-catalog/scripts/catalog_content.py \
  --url "<your_site_url>" \
  --output json

Map all existing content:

• Blog posts by topic
• Resource pages, guides, glossary
• Landing pages with content
• Identify which topics you already have content for
• Note: thin pages, outdated content, orphan pages (no internal links)

1B: Competitor Content Mapping

For each competitor, run site-content-catalog:

python3 skills/site-content-catalog/scripts/catalog_content.py \
  --url "<competitor_url>" \
  --output json

Map their content architecture:

• How do they structure topic clusters?
• Which topics have pillar pages?
• How deep do their clusters go?
• Internal linking patterns
• Content freshness (update dates)

1C: Domain Authority Baseline

Run seo-domain-analyzer for your site and competitors:

• Your domain authority vs. competitors
• Keyword overlap analysis
• Where competitors rank that you don't

Phase 2: Topic Universe Expansion

2A: Subtopic Discovery

For each target topic area, generate the full subtopic universe:

Enhanced mode (DataForSEO / Keywords Everywhere):

# DataForSEO keyword suggestions
POST /v3/dataforseo_labs/google/keyword_suggestions/live
{
  "keyword": "<topic>",
  "limit": 500
}

# DataForSEO related keywords
POST /v3/dataforseo_labs/google/related_keywords/live
{
  "keyword": "<topic>",
  "limit": 500
}

Extract:

• All related keywords and questions
• Search volumes per keyword
• Keyword difficulty scores
• Semantic groups (auto-clustered by meaning)

Baseline mode:

Use multiple sources to build the subtopic list:

• web_search for "topic + [what/how/why/best/vs/guide/examples]"
• reddit-post-finder for questions people ask about the topic
• Google autocomplete patterns (via web search)
• Competitor content titles (from Phase 1B)
• PAA questions from search results

2B: Question Mining

Run reddit-post-finder for each topic area:

python3 skills/reddit-post-finder/scripts/search_reddit.py \
  --subreddit "<relevant_subs>" \
  --keywords "<topic>" \
  --days 365 --sort top --time year

Extract:

• Questions people ask (→ individual article topics)
• Recurring themes (→ cluster pillars)
• Misconceptions (→ myth-busting content)
• Comparisons people make (→ vs/ content)
• Use cases discussed (→ use-case content)

2C: Keyword Clustering

Group all discovered keywords/subtopics into semantic clusters:

Enhanced mode: Use DataForSEO keyword clustering API or group by SERP overlap (keywords that share 3+ ranking URLs likely belong to the same cluster).

Baseline mode: Manual semantic grouping based on:

• Shared root concepts
• User intent alignment (informational / commercial / navigational)
• Topic hierarchy (broad → specific)

Phase 3: Cluster Architecture

3A: Pillar-Cluster Mapping

For each topic area, design the cluster hierarchy:

PILLAR: [Broad Topic] — "The Complete Guide to [Topic]"
│
├── CLUSTER 1: [Subtopic Group A]
│   ├── Article: [Specific subtopic A1]
│   ├── Article: [Specific subtopic A2]
│   └── Article: [Specific subtopic A3]
│
├── CLUSTER 2: [Subtopic Group B]
│   ├── Article: [Specific subtopic B1]
│   ├── Article: [Specific subtopic B2]
│   └── Article: [Specific subtopic B3]
│
├── CLUSTER 3: [Subtopic Group C]
│   ├── Article: [Specific subtopic C1]
│   └── Article: [Specific subtopic C2]
│
└── SUPPORTING: [Glossary terms, FAQs, tools]
    ├── Glossary: [Term 1]
    ├── Glossary: [Term 2]
    └── FAQ: [Common questions]