search-engineer
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.
Mit meinem Agent installieren
Kopieren Sie diese Anfrage in Ihren Agent. Sie enthält die maßgebliche Skill-Seite und das Manifest.
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.Ihr Agent sollte weiterhin seinen Plan anzeigen und alle von der Sicherheitsrichtlinie verlangten Bestätigungen anfordern.
Agent-lesbare Ressourcen
Verwenden Sie diese Links, wenn ein KI-Agent, Crawler oder Skript sauberen Kontext benötigt, statt die vollständige Seite zu lesen.
Testen
„search-engineer“ wird verwendet. Recommend a search architecture for two million Chinese product records with filters and semantic queries.
Erwartetes Ergebnis:
Use Elasticsearch for structured filters and lexical recall, plus a vector field for semantic retrieval. Evaluate hybrid ranking with NDCG and conversion metrics.
„search-engineer“ wird verwendet. Our zero-result rate increased after changing the analyzer.
Erwartetes Ergebnis:
- 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.
„search-engineer“ wird verwendet. Plan evaluation for a new RAG retriever.
Erwartetes Ergebnis:
Build a judged query set, measure recall at several depths, evaluate reranking with NDCG, and track answer faithfulness against retrieved evidence.
Sicherheitsaudit
SicherThe 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.
Elemente der Fähigkeitsprüfung (1)
Dies sind echte lokale Fähigkeiten, die für diese Fähigkeit erwartet werden können; daher müssen sie überprüft werden, werden jedoch nicht als bestätigtes bösartiges Verhalten gezählt.
Risikofaktoren
⚙️ Externe Befehle (25)
🌐 Netzwerkzugriff (1)
Diesen Bericht teilen & zitieren
Teile den versionierten Bewertungsbericht, das neutrale Badge, die Einbettungskarte und Zitate. Skillstore berichtet Nachweise, ohne zu entscheiden, ob dieser Skill sicher ist.
Berichtslink kopieren
https://skillstore.io/skills/zl2023github-search-engineer/audits/4?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown-Badge
[](https://skillstore.io/skills/zl2023github-search-engineer?utm_source=security_passport_badge)HTML-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>Einbettungskarte
<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>Wissenschaftliche Zitate (APA · BibTeX · CFF)
APA-Zitat
zl2023github. (2026). search-engineer security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/zl2023github-search-engineer/audits/4BibTeX-Zitat
@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"
Skillstore-Score
Warum dieser Score Evidenzvertrauen: MittelWas Sie erstellen können
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.
Diese Prompts ausprobieren
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.
Bewährte Praktiken
- 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.
Vermeiden
- 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.
Häufig gestellte Fragen
Does this skill install or run a search engine?
Which search technologies are covered?
Can it help with Chinese search?
Does it support semantic search and RAG?
How does it evaluate search quality?
Is the example code production ready?
Entwicklerdetails
Autor
zl2023githubLizenz
MIT
Skillstore-Revision
r2
Versionshinweis
Der Autor hat keine Version angegeben.
Ref.
88a8e9a07f4c54ab105c1c41b6267c287146b07b
Aktualität der Wartung
29.7.2026
Nutzung
5 Downloads · 4 Aufrufe
Dateistruktur
📄 SKILL.md