Cluster Command

Build a complete topic cluster strategy with pillar page definition, 8-12 supporting articles, internal linking map, and creation sequence.

Usage

/cluster [topic]

Examples:

• /cluster "content marketing"
• /cluster "podcast monetization"
• /cluster "remote team management"

Process

Step 1: Gather Existing Data

Check for existing research that informs this cluster:

1. Search research/ for any existing /research-topics output:

   Glob: research/topic-clusters-*.md

1. Search for any existing research on this topic:

   Glob: research/*[topic-slug]*.md

1. If found, extract:

• Authority score for this topic area
• Keywords already ranking
• Coverage gaps identified
• Any SERP analysis already done

Document what exists vs. what needs fresh research.

Step 2: Keyword Research

Build the complete keyword landscape for this topic.

1. DataForSEO Keyword Ideas

   python3 -c "
   import sys; sys.path.insert(0, 'data_sources/modules')
   from dataforseo import DataForSEOClient
   client = DataForSEOClient()
   results = client.get_keyword_ideas('$ARGUMENTS')
   for kw in sorted(results, key=lambda x: x.get('search_volume', 0), reverse=True)[:30]:
       print(f\"{kw.get('keyword', 'N/A')} | Vol: {kw.get('search_volume', 'N/A')} | Diff: {kw.get('keyword_difficulty', 'N/A')} | CPC: {kw.get('cpc', 'N/A')}\")
   "

1. DataForSEO Questions

   python3 -c "
   import sys; sys.path.insert(0, 'data_sources/modules')
   from dataforseo import DataForSEOClient
   client = DataForSEOClient()
   results = client.get_questions('$ARGUMENTS')
   for q in results[:15]:
       print(f\"{q.get('keyword', 'N/A')} | Vol: {q.get('search_volume', 'N/A')}\")
   "

1. WebSearch for Additional Keywords

   WebSearch: "[topic] guide" site:ahrefs.com OR site:semrush.com OR site:moz.com
   WebSearch: "[topic] related keywords" OR "[topic] subtopics"

1. Group Keywords into Tiers

• Pillar-level: Broad, high-volume (1000+ searches/mo), competitive
• Supporting-level: Specific subtopics, medium volume (100-1000/mo)
• Long-tail: Very specific queries, low volume (<100/mo), low competition

Step 3: SERP Analysis

Analyze what's winning for the pillar keyword.

1. Get SERP Data

   python3 -c "
   import sys; sys.path.insert(0, 'data_sources/modules')
   from dataforseo import DataForSEOClient
   client = DataForSEOClient()
   results = client.get_serp_data('$ARGUMENTS')
   for r in results[:10]:
       print(f\"Position {r.get('position', 'N/A')}: {r.get('title', 'N/A')}\")
       print(f\"  URL: {r.get('url', 'N/A')}\")
       print(f\"  Description: {r.get('description', 'N/A')[:100]}\")
       print()
   "

1. Fetch Top 3 Pillar/Guide Pages

Use WebFetch on the top 3 ranking comprehensive guides. For each, document:

• H2/H3 structure
• Word count estimate
• Topics covered
• Gaps and thin sections
• Unique angles or data

1. Identify Differentiation Opportunities

• What do all top results miss?
• Where are they thin or generic?
• What unique angle can we bring?
• What data or examples are outdated?

Step 4: Define Pillar Page

Adopt the cluster-strategist agent role (@agents/cluster-strategist.md).

Define the pillar page:

Pillar Page Outline:

• Create full H2/H3 outline
• Each H2 should map to a supporting article topic
• Include sections that competitors cover (Google-validated structure)
• Add sections that fill identified gaps
• Note where each supporting article will be linked from

Step 5: Define Supporting Articles (8-12)

For each supporting article, specify:

Prioritization Framework:

• Volume (30%): Higher search volume = higher score
• Difficulty Inverse (20%): Lower difficulty = higher score
• Commercial Intent (20%): Closer to conversion = higher score
• Pillar Dependency (15%): More essential to pillar completeness = higher score
• Cross-link Va