name: keyword-research description: "Standalone keyword research — expands seeds via the brand's connected keyword MCP (Ahrefs, Semrush, SE Ranking, or GSC), classifies search intent, maps keywords to content types, surfaces competitor content gaps, long-tail and SERP-feature opportunities, and delivers a prioritized keyword strategy document. Volume and difficulty come from the connected provider and are never fabricated. Triggers on "/digital-marketing-pro:keyword-research", "what keywords should we target", "find content gaps versus competitors", "expand these seed keywords", "which queries have buying intent". Reads the brand profile, guidelines, and campaign history; hands 20+ raw keywords to /digital-marketing-pro:keyword-cluster for pillar+spokes clustering." argument-hint: "[topic or seed keywords]"

/digital-marketing-pro:keyword-research

Purpose

Standalone keyword research tool — expansion, search-intent classification, and competitor gap analysis. Produces a prioritized, intent-classified keyword list with content recommendations. Volume and keyword-difficulty figures come from the brand's connected keyword MCP (Ahrefs / Semrush / SE Ranking / GSC) — this skill surfaces and interprets them, it does not fabricate them. Clustering into a pillar+spokes plan is delegated to /digital-marketing-pro:keyword-cluster (the keyword_cluster.py engine); this skill produces the seeds that skill consumes.

Input Required

The user must provide (or will be prompted for):

• Seed keywords or topic: Starting keywords, a topic area, or a URL to extract keyword themes from
• Target audience: Who the content is intended to reach (demographics, expertise level, pain points)
• Industry: The vertical or niche to contextualize volume and difficulty estimates
• Competitor domains: Optional -- 1-3 competitor domains to run content gap analysis against
• Target market/language: Geographic and language targeting for volume estimates
• Content goals: Traffic, leads, thought leadership, product sales, or brand awareness
• Existing content inventory: Optional -- URLs or topics already published to avoid duplication

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply voice, compliance, industry context. Check guidelines/_manifest.json for restrictions, messaging, channel styles, voice-and-tone rules, and templates. If a template matching this command exists in ~/.claude-marketing/brands/{slug}/templates/, apply its format. If no brand exists, prompt for /digital-marketing-pro:brand-setup or proceed with defaults.
2. Check campaign history: Run python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns to identify previous keyword research and content campaigns to build upon rather than duplicate.
3. Load reference files: Consult skills/content-engine/ for content strategy context and skills/context-engine/industry-profiles.md for industry-specific keyword benchmarks and search behavior patterns.
4. Expand the seed set: Use the brand's connected keyword MCP (Ahrefs getRelatedKeywords, Semrush, SE Ranking, or GSC query mining) to expand seeds into a candidate list, pulling provider volume and keyword-difficulty figures where available. Record the provider and pull date — volume/KD are provider estimates, not measurements, and providers disagree by 20-50%. Do not claim volume/KD/trend numbers the connected tools didn't return.
5. Classify search intent: Categorize every keyword into intent buckets -- informational (how-to, what-is), navigational (brand, product names), commercial (best, reviews, comparison), and transactional (buy, pricing, demo, free trial).
6. Map keywords to content types: Assign each cluster a recommended content format -- blog post, landing page, pillar page, comparison page, FAQ, video, tool, or interactive content -- based on intent and SERP feature analysis.
7. Identify content gaps vs competitors: If competitor domains were provided, cross-reference their ranking keywords against the brand's current coverage to surface missed opportunities and underserved topics.
8. Discover long-tail opportunities: Expand each cluster with long-tail variants, question-based keywords (People Also Ask patterns), and related search modifiers that represent lower-difficulty entry points.
9. Assess SERP feature opportunities: For each primary keyword, identify which SERP features are present (featured snippets, People Also Ask, knowledge panels, image packs, video carousels) and note which are attainable.
10. Identify seasonal and trending opportunities: Flag keywords with notable seasonal patterns or rising search trends that present time-sensitive content opportunities requiring prioritized scheduling.
11. Prioritize by impact and difficulty: Score each keyword cluster on a composite priority metric weighing estimated volume, ranking difficulty, business relevance, conversion potential, and content gap opportunity.
12. Generate keyword strategy document: Compile the full analysis into a structured deliverable with clear next-step recommendations for content creation sequencing.

Output

A structured keyword strategy document containing:

• Keyword clusters organized by topic theme, each with individual keywords listed
• Estimated monthly search volume and keyword difficulty per keyword
• Search intent classification (informational, navigational, commercial, transactional) per keyword
• SERP feature opportunities per cluster (featured snippets, PAA, video, image pack)
• Recommended content type and format for each cluster
• Priority score (high/medium/low) with rationale for sequencing
• Content gap analysis showing competitor-owned keywords the brand is missing
• Long-tail keyword opportunities with lower difficulty and high relevance
• Question-based keyword list for FAQ and People Also Ask targeting
• Recommended content creation roadmap based on priority ranking
• Quick-win keywords (low difficulty, decent volume, high relevance) flagged for immediate action
• Seasonal or trending keyword opportunities with timing recommendations
• Internal linking opportunities between keyword clusters and existing content

Tips & caveats

• Search volume from any provider is an estimate. Ahrefs, Semrush, GSC, SE Ranking all disagree by 20-50% on the same keyword. Use ranges, not point estimates.
• Keyword difficulty (KD) is a heuristic, not a measurement. A KD of 60 means "competitive" — not "impossible". A small brand with niche authority can rank for KD-70 keywords against generalist KD-30 sites.
• Long-tail isn't always lower-volume. With AI search rewriting queries, the actual click-driving query may differ from the seed. Always check the resulting query a user typed via GSC, not the rank-tracker assumption.
• Hand off to /digital-marketing-pro:keyword-cluster once you have ≥ 20 raw keywords. Clustering before writing is what produces topical authority, not keyword lists.
• Don't research the same keyword set quarterly. Re-research only when business model, target market, or competitive landscape changes. Otherwise the deltas are noise.
• Intent classification beats volume. A "buy [product]" query at 200/mo is worth more than "what is [product]" at 5000/mo for most commercial brands.

Agents Used

• seo-specialist -- Keyword research, volume and difficulty estimation, SERP analysis, content gap identification, and priority scoring
• content-creator -- Content type mapping, content angle recommendations, and editorial planning for keyword-targeted pieces