Habilidades search-engineer
📦

search-engineer

Revisão do conteúdo r2 Seguro ⚙️ Comandos externos🌐 Acesso à rede

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.

Suporta: Claude Codex Code(CC)
🥉 77 Bronze

Instalar com meu Agente

Copie esta solicitação para o seu Agente. Ela inclui a página canônica do Skill e o manifesto.

Solicitação do agente
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.

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Detalhe em Markdown GET /skills/zl2023github-search-engineer.md manifest assinado GET /api/skills/zl2023github-search-engineer/manifest lockfile assinado GET /api/skills/zl2023github-search-engineer/lockfile

Testar

Usando "search-engineer". Recommend a search architecture for two million Chinese product records with filters and semantic queries.

Resultado esperado:

Use Elasticsearch for structured filters and lexical recall, plus a vector field for semantic retrieval. Evaluate hybrid ranking with NDCG and conversion metrics.

Usando "search-engineer". Our zero-result rate increased after changing the analyzer.

Resultado esperado:

  • Compare token output before and after the analyzer change.
  • Segment affected queries by language, category, and frequency.
  • Restore recall with synonyms or analyzer adjustments, then run an A/B test.

Usando "search-engineer". Plan evaluation for a new RAG retriever.

Resultado esperado:

Build a judged query set, measure recall at several depths, evaluate reranking with NDCG, and track answer faithfulness against retrieved evidence.

Auditoria de segurança

Seguro
v4 • 24/07/2026 Abrir o relatório versionado

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.

1
Arquivos analisados
310
Linhas analisadas
1
Itens de revisão
0
Falsos positivos ignorados
Itens de revisão de capacidade (1)

Estas são capacidades locais reais que podem ser esperadas para esta habilidade, portanto exigem revisão, mas não são contabilizadas como comportamento malicioso confirmado.

Baixo
SKILL.md:88
Hardcoded URL
./bin/elasticsearch-plugin install https://github.com/medcl/elasticsearch-analysis-ik/releases/downl
Line 88 directs the plugin installer to download a third-party release archive from GitHub. The version is pinned, but the example provides no checksum or signature verification.

Fatores de risco

⚙️ Comandos externos (25)
SKILL.md:17-25 SKILL.md:25-28 SKILL.md:28-42 SKILL.md:42-45 SKILL.md:45-56 SKILL.md:56-61 SKILL.md:61-81 SKILL.md:81-86 SKILL.md:86-93 SKILL.md:93-96 SKILL.md:96-116 SKILL.md:116-121 SKILL.md:121-141 SKILL.md:141-146 SKILL.md:146-164 SKILL.md:164-168 SKILL.md:168-188 SKILL.md:188-192 SKILL.md:192-210 SKILL.md:210-215 SKILL.md:215-237 SKILL.md:237-241 SKILL.md:241-259 SKILL.md:259-289 SKILL.md:289-300
🌐 Acesso à rede (1)
SKILL.md:88
Auditado por: codex Ver histórico de auditoria →
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Citações acadêmicas (APA · BibTeX · CFF)

Citação APA

zl2023github. (2026). search-engineer security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/zl2023github-search-engineer/audits/4

Citação BibTeX

@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} }

CITATION.cff

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"

Pontuação do Skillstore

Por que esta pontuação Confiança das evidências: Médio
55
Arquitetura
85
Manutenibilidade
87
Conteúdo
65
Comunidade
83
Conformidade com a especificação

O que você pode criar

Plan a Search Platform

Compare keyword, vector, and hybrid designs, then define mappings, shards, analyzers, and evaluation criteria.

Build a RAG Retriever

Select embeddings, chunk documents, configure vector retrieval, and define relevance measurements for a RAG pipeline.

Diagnose Search Quality

Review zero-result queries, clicks, latency, and A/B metrics to identify practical ranking improvements.

Teste estes prompts

Choose a Search Engine 
Compare Elasticsearch, Solr, and Milvus for my dataset, query patterns, scale, latency target, and language requirements. Recommend one architecture with reasons.
Design an Index 
Design an Elasticsearch index for these fields: [fields]. Include mappings, analyzers, shard assumptions, filters, sorting, and a relevance evaluation plan.
Investigate Relevance Problems 
Analyze these search metrics and bad cases: [data]. Classify recall, ranking, tokenization, and latency issues. Propose prioritized experiments with success metrics.
Architect Hybrid RAG Retrieval 
Create a production design for hybrid RAG retrieval using [data source]. Specify chunking, embeddings, lexical recall, vector recall, reranking, caching, and evaluation.

Boas práticas

  • Define representative queries and relevance judgments before tuning retrieval.
  • Validate changes offline, then use controlled experiments with business and quality metrics.
  • Pin dependencies, secure production services, and verify third-party artifacts before installation.

Evitar

  • Do not select an engine before documenting data, query, scale, and latency requirements.
  • Do not optimize only click-through rate without relevance and conversion safeguards.
  • Do not copy example commands into production without authentication, backups, and environment review.

Perguntas frequentes

Does this skill install or run a search engine?
No. It provides guidance and examples. Users must review and execute commands in their own environment.
Which search technologies are covered?
The examples cover Elasticsearch, Milvus, Redis, sentence transformers, Chroma, and related Python analysis libraries.
Can it help with Chinese search?
Yes. It discusses Chinese analyzers, custom dictionaries, query normalization, synonyms, and Chinese embedding models.
Does it support semantic search and RAG?
Yes. It outlines embeddings, vector indexes, chunking, retrieval, reranking considerations, and evaluation.
How does it evaluate search quality?
It covers NDCG, MAP, click metrics, zero-result rate, latency, and statistical A/B comparison.
Is the example code production ready?
No. Add error handling, security, observability, dependency pinning, tests, and environment-specific configuration before production use.

Detalhes do desenvolvedor

Licença

MIT

Revisão da Skillstore

r2

Aviso de versão

O autor não declarou uma versão.

Repositório

https://github.com/zl2023github/software-engineer-skills/tree/main/software-engineering/search-engineer

Referência

88a8e9a07f4c54ab105c1c41b6267c287146b07b

Atualidade da manutenção

26/07/2026

Uso

5 downloads · 3 visualizações

Estrutura de arquivos

📄 SKILL.md