Build and Optimize Search Systems
Search teams need reliable guidance across indexing, ranking, semantic retrieval, and evaluation. This skill provides practical workflows and examples for building and improving search systems.
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Review the Skillstore skill "search-engineer" from https://skillstore.io/skills/zl2023github-search-engineer.md and its manifest at https://skillstore.io/api/skills/zl2023github-search-engineer/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
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Using "search-engineer". Recommend a search architecture for two million Chinese product records with filters and semantic queries.
Expected outcome:
Use Elasticsearch for structured filters and lexical recall, plus a vector field for semantic retrieval. Evaluate hybrid ranking with NDCG and conversion metrics.
Using "search-engineer". Our zero-result rate increased after changing the analyzer.
Expected outcome:
Using "search-engineer". Plan evaluation for a new RAG retriever.
Expected outcome:
Build a judged query set, measure recall at several depths, evaluate reranking with NDCG, and track answer faithfulness against retrieved evidence.
The 25 external-command findings are false positives caused by Markdown code fences; the document does not use Ruby or shell backtick execution. The pinned third-party plugin installation is a genuine low-severity network and supply-chain risk because no checksum or signature verification is provided.
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
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https://skillstore.io/skills/zl2023github-search-engineer/audits/4?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/zl2023github-search-engineer?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/zl2023github-search-engineer?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/zl2023github-search-engineer/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/zl2023github-search-engineer.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>zl2023github. (2026). search-engineer security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/zl2023github-search-engineer/audits/4@techreport{zl2023github-zl2023github-search-engineer-2026,
author = {zl2023github},
title = {search-engineer security audit report (audit version 4)},
institution = {Skillstore},
year = {2026},
number = {4},
url = {https://skillstore.io/skills/zl2023github-search-engineer/audits/4},
note = {Author version unspecified}
}cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "search-engineer security audit report (audit version 4)"
version: "unspecified"
type: report
authors:
- name: "zl2023github"
date-released: "2026-07-24"
url: "https://skillstore.io/skills/zl2023github-search-engineer/audits/4"
identifiers:
- type: other
value: "skillstore:zl2023github-search-engineer:audit:4"
description: "Skillstore immutable audit report identifier"
Compare keyword, vector, and hybrid designs, then define mappings, shards, analyzers, and evaluation criteria.
Select embeddings, chunk documents, configure vector retrieval, and define relevance measurements for a RAG pipeline.
Review zero-result queries, clicks, latency, and A/B metrics to identify practical ranking improvements.
Compare Elasticsearch, Solr, and Milvus for my dataset, query patterns, scale, latency target, and language requirements. Recommend one architecture with reasons.
Design an Elasticsearch index for these fields: [fields]. Include mappings, analyzers, shard assumptions, filters, sorting, and a relevance evaluation plan.
Analyze these search metrics and bad cases: [data]. Classify recall, ranking, tokenization, and latency issues. Propose prioritized experiments with success metrics.
Create a production design for hybrid RAG retrieval using [data source]. Specify chunking, embeddings, lexical recall, vector recall, reranking, caching, and evaluation.
Author
zl2023githubLicense
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
88a8e9a07f4c54ab105c1c41b6267c287146b07b
Maintenance freshness
7/26/2026
Usage
4 downloads · 3 views
File structure
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