SEO keyword cluster map

Purpose

Turn raw keyword data into a practical topic architecture that assigns intent, primary targets, supporting questions, existing coverage, and content gaps.

Scope

The skill covers normalization, intent, similarity, clustering, canonical target selection, page mapping, and roadmap priorities.

Excluded: keyword purchasing, ranking guarantees, automated page creation, and unsupported search-volume forecasts.

Data basis

• Keyword table supplied as an input.
• Audience, site inventory, product and editorial context in scope.

Result

A cluster sheet and a short content roadmap.

Quality criteria

• Every keyword appears once or has a documented merge decision.
• Clusters reflect intent and audience need, not only word overlap.
• Each primary target has a page action and evidence basis.
• Unknown metrics remain unknown.

Instructions

Normalize spelling and modifiers without losing meaningful distinctions. Separate informational, navigational, commercial and transactional intent. Use existing pages as evidence for consolidation, not as proof that a topic is complete. Mark uncertain clusters for editorial review.

Adapt before use

• Map keyword columns and metric definitions.
• Add audience segments, site inventory and product taxonomy.
• Define canonical-page and similarity rules.
• Define roadmap priority criteria.
• Keep source facts, analysis, recommendations, and unresolved questions visibly separate.
• Preserve stable document, section, page, sentence, URL, or row identifiers in every finding.
• Record assumptions about audience, channel, period, approval status, and data coverage.
• Treat missing guidance as an adaptation gap rather than inventing a company rule.
• Prefer a precise, bounded recommendation over a broad generic rewrite.
• Use supplied terminology consistently across the document, sheet, and any derivative copy.
• Do not infer performance, customer behavior, product capability, or market response from wording alone.
• Make every unresolved issue actionable by naming the missing decision, evidence, owner, or source.
• Keep factual claims traceable even when the final copy is concise.
• Recheck that counts, names, dates, and identifiers match across all outputs.
• Mark hypotheses as hypotheses and preserve competing explanations when evidence is mixed.
• Use the stated acceptance criteria as the final completeness check.