| name | marketplace-search-recsys-planning |
| description | Search and recommendation system planning for a two-sided trust marketplace built on OpenSearch โ user-intent framing, product-surface architecture, index design, query understanding, retrieval strategy, ranking, search-plus-recs blending, measurement, and a dashboard-and-alerting layer for ongoing decision making. Triggers on tasks involving marketplace search, homefeeds, ranking, relevance tuning, OpenSearch query DSL, analyzers, synonyms, golden sets, NDCG, A/B testing, or diagnosing an existing retrieval system. Use this skill BEFORE marketplace-personalisation when planning new work; hand off when the diagnosed bottleneck is personalisation-specific. |
Marketplace Engineering Two-Sided Search and Recsys Planning Best Practices
Comprehensive planning, design and diagnostic guide for search and recommendation systems
in two-sided trust marketplaces. Covers OpenSearch index, query and ranking patterns, the
methodology for planning retrieval work, the handoff points to recommendation-specific
tooling, and the instrumentation and dashboard layer that turns measurement into ongoing
decision making. Contains 57 rules across 10 categories ordered by cascade impact, plus
two playbooks (plan a new system from scratch, diagnose an existing one) and explicit
living-artefact conventions (decisions log, golden set, gotchas).
When to Apply
Reference this skill when:
- Planning a new marketplace retrieval project from scratch
- Reviewing an existing retrieval system that feels stale, unfair, or unpersonalised
- Designing the OpenSearch index mapping, analyzers, or query DSL
- Choosing retrieval primitives per product surface (search, recs, hybrid, curated)
- Deciding which search quality metrics to track and dashboard
- Running the weekly search-quality review ritual
- Diagnosing a silent regression in ranking, coverage, or zero-result rate
- Deciding when a retrieval problem is actually a personalisation problem
This skill is the precursor to marketplace-personalisation. Start here for
planning and search work; hand off to the personalisation skill when the diagnosed
bottleneck is impression tracking, feedback-loop bias, or AWS Personalize-specific
design.
