seo-research

SEO Research

Purpose

Build a decision-ready research note for traditional/web-search content and site decisions. This skill is for scoped discovery, not for a fabricated keyword spreadsheet, mass scraping, or a substitute for the immutable multi-engine ai-search-research Research Pack.

Read references/research-evidence-protocol.md before presenting research conclusions. Read query-opportunity-protocol.md when the goal is to choose what the site should rank for or identify the strongest next query opportunity.

Input gate

Clarify audience, market/locale, decision to be made, known site/entity URLs, time horizon, access/permissions, and available first-party data. Decide whether the question is conventional search research or AI-search research:

• Use ai-search-research for engine/surface-specific AI evidence, ground truth, citations, or formal Research Pack provenance.
• Use this skill for content/search intent, page inventory, query language, competitor/page observations, and source-backed opportunity framing without that formal contract.

Workflow

1. Define the decision. State the audience task, market/locale, entity/page scope, non-goals, and evidence required to decide.
2. Build a query/question corpus. Separate user needs, observed search queries, AI prompts, and engine-executed subqueries. Group language by task and stage, not just lexical similarity. Preserve parent families, source, date, locale, country/location, device, engine/surface, conversation turn, coverage, confidence, and limitations. Hash-pin local source and coverage evidence. Treat volume/difficulty/vendor metrics as dated estimates, never ground truth.
3. Inspect existing coverage and performance. Map actual pages/assets to the questions they serve. When authorized first-party data exists, preserve query, page, country, device, search type, time window, clicks, impressions, CTR, and average position together; identify head-term/category leadership, near-win visibility, snippet/CTR, coverage-gap, defend, and cannibalization candidates without treating average position as an exact rank. Absence from first-party rows does not remove an owner-mandated high-volume family; retain it with the demand/SERP evidence and record the visibility gap.
4. Inspect the current result set. For candidate query families, record a dated locale/device SERP observation: dominant intent, result/page types, visible title/snippet patterns, strong competitor evidence, freshness, authority expectations, and features that change the reader task. A SERP snapshot is evidence for fit, not a stable ranking formula.
5. Select page-level query ownership. Choose one primary need/query family and natural supporting language per target page. Preserve owner-named strategic queries, business value, achievable page fit, existing authority, coverage state, and evidence gaps. The highest-volume phrase is not automatically the best short-term opportunity, but every owner-declared relevant high-volume family and the market's highest-relevant-demand family must receive an explicit head-term leadership decision. Difficulty changes the route; it does not erase the ambition.
6. Research external evidence responsibly. Use primary docs, authoritative sources, and accessible pages. Capture title, URL, access date, source type, claim, and limitations. Respect robots, terms, rate limits, paywalls, authentication, and copyright.
7. Synthesize choices. Return a two-track opportunity slate: nearer-term capture/defense work and high-volume head-term/category leadership. Use now, next, and later to sequence both tracks without allowing easy work to replace the leadership target. Use defer for a documented owner decision, genuine intent/product mismatch, or prohibited/misleading target—not merely high difficulty, weak authority, cost, or current absence. Separate observed facts, inferred opportunities, experiments, and unknowns; do not collapse unlike dimensions into a universal keyword score.
8. Freeze formal query work. Let <suite-root> mean ${CLAUDE_PLUGIN_ROOT} in Claude Code. In Codex, read .seo-suite-runtime.json beside this SKILL.md when present and use its suite_root value; otherwise use the absolute repository checkout. When the corpus will drive a baseline, audit, page map, or implementation decision, create query-corpus.json using the checked-out suite contract and validate it with python "<suite-root>/scripts/validate_query_corpus.py" validate-corpus <bundle>/query-corpus.json --bundle <bundle>.
9. Hand off. Send conventional performance measurement to seo-performance, page search-result review to seo-page, content work to seo-content, competitor pages to seo-competitor-pages, implementation sequencing to seo-action-plan, and AI-specific formal work to ai-search-research.