Prompt
Find your landing page message
Connect a product’s value to a specific audience and write a clear first-screen message.
ClaudeChatGPTGeminiMarketing
Dave AI
Search and Merchandising
Search and merchandising for e-commerce: product search relevance tuning, faceted navigation, recommendation engine patterns (collaborative filtering, content-based), A/B testing for layouts, personalization, and SEO for product pages.
instructions.md
--- name: domain-ecommerce-search-merchandising-5b06e42ac232 description: | Search and merchandising for e-commerce: product search relevance tuning, faceted navigation, recommendation engine patterns (collaborative filtering, content-based), A/B testing for layouts, personalization, and SEO for product pages. allowed-tools: Read, Grep, Glob, Bash --- # Search and Merchandising ## When to use - Selecting or tuning a product search engine (Elasticsearch, Algolia, Typesense, Meilisearch) - Implementing or improving search relevance (field boosting, synonyms, function scoring) - Building faceted navigation with dynamic facet counts and URL-preserving filters - Implementing a recommendation engine (collaborative filtering, content-based, or hybrid) - Running A/B tests on search ranking, listing layouts, or recommendation placements - Adding personalization to search results, homepage, or email recommendations - Implementing technical SEO for product pages (schema.org, canonical URLs, hreflang, sitemaps) ## Core principles 1. **Boost by behavior, not just text match** — sales count and view count as function score signals outperform pure BM25 for commerce 2. **Facet counts must stay accurate under multi-select** — post-filter aggregations; users abandon nav when counts are wrong 3. **Cold start kills collaborative filtering** — always have a content-based fallback for new users and new products 4. **Canonical every filter combination** — faceted nav generates thousands of URL permutations; without canonicals, you split ranking signals 5. **Hash user ID for A/B assignment** — session-based assignment causes variant flicker on login; user ID gives consistent experience ## Reference Files - `references/search-facets-recommendations.md` — search engine selection, relevance tuning, autocomplete, facet types and implementation, collaborative/content-based/hybrid recommendation patterns and placement strategy - `references/abtesting-personalization-seo.md` — what to A/B test in search and merchandising, feature flag implementation, personalization pipeline, schema.org markup, technical SEO for product pages--- name: domain-ecommerce-search-merchandising-5b06e42ac232 description: | Search and merchandising for e-commerce: product search relevance tuning, faceted navigation, recommendation engine patterns (collaborative filtering, content-based), A/B testing for layouts, personalization, and SEO for product pages. allowed-tools: Read, Grep, Glob, Bash --- # Search and Merchandising ## When to use - Selecting or tuning a product search engine (Elasticsearch, Algolia, Typesense, Meilisearch) - Implementing or improving search relevance (field boosting, synonyms, function scoring) - Building faceted navigation with dynamic facet counts and URL-preserving filters - Implementing a recommendation engine (collaborative filtering, content-based, or hybrid) - Running A/B tests on search ranking, listing layouts, or recommendation placements - Adding personalization to search results, homepage, or email recommendations - Implementing technical SEO for product pages (schema.org, canonical URLs, hreflang, sitemaps) ## Core principles 1. **Boost by behavior, not just text match** — sales count and view count as function score signals outperform pure BM25 for commerce 2. **Facet counts must stay accurate under multi-select** — post-filter aggregations; users abandon nav when counts are wrong 3. **Cold start kills collaborative filtering** — always have a content-based fallback for new users and new products 4. **Canonical every filter combination** — faceted nav generates thousands of URL permutations; without canonicals, you split ranking signals 5. **Hash user ID for A/B assignment** — session-based assignment causes variant flicker on login; user ID gives consistent experience ## Reference Files - `references/search-facets-recommendations.md` — search engine selection, relevance tuning, autocomplete, facet types and implementation, collaborative/content-based/hybrid recommendation patterns and placement strategy - `references/abtesting-personalization-seo.md` — what to A/B test in search and merchandising, feature flag implementation, personalization pipeline, schema.org markup, technical SEO for product pages
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.