ai-search-research

AI Search Research

Produce Research Pack 1.1.0 and a hash-bound query-corpus.json before any human summary. Research Pack 1.1.0 SHA-256-pins evidence, competitor observations, and ground-truth provenance; legacy 1.0.0 remains readable. Treat the Research Pack as authoritative for evidence and ground truth; treat the Query Corpus as authoritative for user needs, prompts, observed queries, executed subqueries, demand provenance, and page coverage.

Procedure

1. Define scope, native locales, entities, ambiguities, audiences, journey stages, engines, and distinct surfaces. Cover every declared locale with native query objects; never force an undeclared locale. Select audiences from the request scope; do not force unrelated audiences. Keep Google AI Overview and AI Mode separate.
2. Read evidence-policy.md. Record every externally testable claim as one evidence record with one resolving raw_evidence_ref and matching raw_evidence_sha256; do not merge claims or omit explicit scope.
3. Read capture-protocol.md. Preserve raw observations inside the artifact bundle and use only bundle-relative references.
4. Write each Research Pack query for every declared locale as an independent native object with locale, intent, request-derived audience, journey stage, target entities, and engine/surface applicability. Use native orthography and script. Reject ASCII transliteration presented as native when the locale requires its native script or diacritics. Do not infer locale from language, translate mechanically, or apply heuristic language validation in the validator.
5. Expand those stable query IDs into query-corpus.json. Separate user needs, observed search queries, AI prompts, and only directly observed engine-executed subqueries. Preserve source, observation date, country/location, device, engine/surface, parent query, conversation turn, demand status, URL coverage, confidence, and limitations. Hash-pin the Research Pack and every local source/coverage evidence file; never invent demand or hidden fan-out queries.
6. Record a competitor observation only when a dated, SHA-256-pinned raw answer or result capture resolves locally and identifies engine, surface, locale, mentioned entities, and raw cited URLs. Treat source-landscape pages as evidence, not as competitor observations.
7. Record ground truth with stable fact IDs, SHA-256-pinned bundle-relative provenance, and validity windows.
8. Add one explicit gap for every inaccessible engine/surface/locale cell, ambiguity, missing evidence item, or stale input. Choose exactly one stable gap_type with the decision rules in the capture protocol; do not substitute prose synonyms. Use null only where the contract permits it and explain the limitation.
9. Reject guarantees, universal passage lengths, mandatory llms.txt, blanket crawler access, mass FAQ schema, and causal citation-lift claims unless current primary evidence supports that exact scoped statement. Retain an unproven tactic as experimental or speculative only when a valid evidence record supports that classification. When no evidence exists, do not retain a hypothesis; record only a missing_evidence gap with empty related_claim_ids.
10. Validate the Research Pack against references/contracts/research-pack.schema.json. 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, then validate query-corpus.json with python "<suite-root>/scripts/validate_query_corpus.py" validate-corpus <bundle>/query-corpus.json --bundle <bundle>. Stop on missing fields, hash drift, absolute paths, unresolved raw references, stale current claims, or lock drift.
11. Hand direct-answer completeness, clarity, intent coverage, and extractability audits to seo-aeo; hand entity, evidence, source, citation, and documented engine-control audits to seo-geo. Route conventional performance to seo-performance, technical controls to seo-technical, content changes to seo-content, structured data to seo-schema, locale implementation to seo-hreflang, evidence-linked implementation planning to seo-action-plan, explicitly authorized implementation to optimise-seo, and AI trend measurement to ai-visibility-monitor.