📦
content-creator
88Create Brand-Consistent Marketing Content
Marketing teams need content that stays consistent across channels. This skill helps plan, write, analyze, and optimize content for brand voice and SEO.
Writing & Content View
Skills hybrid-search-implementation
📦
hybrid-search-implementation
Content revision r2 High Risk 🌐 Network access⚙️ External commands
Build Reliable Hybrid Search
Single retrieval methods often miss exact terms or semantic matches. This skill helps combine vector and keyword results with practical fusion and reranking patterns.
Supports: Claude Codex Code(CC)
Stop for confirmation before installing.
Review the plan and obtain explicit user consent before changing files.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "hybrid-search-implementation" from https://skillstore.io/skills/sickn33-hybrid-search-implementation.md and its manifest at https://skillstore.io/api/skills/sickn33-hybrid-search-implementation/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "hybrid-search-implementation". Recommend a fusion method for a support RAG system with product codes and natural-language questions.
Expected outcome:
- Start with reciprocal rank fusion because vector and keyword scores are not directly comparable.
- Retrieve a larger candidate set from each source, then fuse ranks and rerank the leading results.
- Evaluate product-code queries separately from descriptive questions.
Using "hybrid-search-implementation". Outline a PostgreSQL hybrid search implementation for technical documentation.
Expected outcome:
- Store text, metadata, and embeddings together, with GIN and vector indexes.
- Run full-text and vector retrieval with bounded candidate sets.
- Fuse rankings, validate metadata field names, and measure recall and latency.
Security Audit
High Riskv5 • 7/24/2026 Open versioned report
All 32 static findings are false positives caused by keywords, Markdown backticks, headings, or a database fetch method. A separate high-severity SQL injection risk exists because the PostgreSQL example interpolates metadata keys into a query.
2
Files scanned
609
Lines analyzed
0
Review items
0
False positives ignored
Confirmed security concerns (1)
High
SQL Injection in Metadata Filter Construction
The PostgreSQL example interpolates each metadata key into SQL text while parameterizing only its value. An untrusted key can alter the generated query.
The filter key is inserted through an f-string into where_clause without validation or identifier quoting. The resulting clause is passed directly to conn.fetch.
Risk Factors
🌐 Network access (1)
⚙️ External commands (2)
Audited by: codex View Audit History →
Share & cite this report
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
Copy report link
https://skillstore.io/skills/sickn33-hybrid-search-implementation/audits/5?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
[](https://skillstore.io/skills/sickn33-hybrid-search-implementation?utm_source=security_passport_badge)HTML badge
<a href="https://skillstore.io/skills/sickn33-hybrid-search-implementation?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/sickn33-hybrid-search-implementation/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/sickn33-hybrid-search-implementation.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
sickn33. (2026). hybrid-search-implementation security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-hybrid-search-implementation/audits/5BibTeX citation
@techreport{sickn33-sickn33-hybrid-search-implementation-2026,
author = {sickn33},
title = {hybrid-search-implementation security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-hybrid-search-implementation/audits/5},
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: "hybrid-search-implementation security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-24"
url: "https://skillstore.io/skills/sickn33-hybrid-search-implementation/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-hybrid-search-implementation:audit:5"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
Higher Skillstore usage
Author / source Skillstore Score Evidence Confidence Skillstore usage Updated
wshobson Recommended
wshobson-hybrid-search-implementation
Skillstore Score 38
Evidence Confidence Medium
Skillstore usage 14
Updated
2026-08-21
sickn33 Current
sickn33-hybrid-search-implementation
Skillstore Score 38
Evidence Confidence Medium
Skillstore usage 8
Updated
2026-08-21
Skillstore Score
Why this score Evidence Confidence: Medium 55
Architecture
85
Maintainability
87
Content
69
Community
91
Spec Compliance
What You Can Build
Improve RAG Retrieval
Combine semantic recall with exact term matching before generation.
Modernize Product Search
Add vector retrieval and fusion to an existing keyword search service.
Plan Retrieval Experiments
Compare fusion strategies, candidate counts, weights, latency, and ranking quality.
Try These Prompts
Choose a Fusion Method
Explain whether reciprocal rank fusion or weighted score fusion fits my search system. My data is [data], and success means [metric].
Design a Hybrid Search Flow
Design a hybrid search flow for [use case] using [vector store] and [keyword engine]. Include candidate retrieval, fusion, and evaluation.
Adapt a Database Implementation
Adapt the hybrid search patterns to [database and version]. Use my schema [schema summary] and constraints [constraints]. Address safe filtering and indexing.
Optimize and Evaluate Retrieval
Create an experiment plan for my hybrid retriever. Compare [methods], define offline and online metrics, control latency, and diagnose query segments.
Best Practices
- Evaluate retrieval with representative queries and relevance judgments.
- Log component ranks and scores so ranking failures remain diagnosable.
- Validate filter fields and parameterize every value before executing database queries.
Avoid
- Do not combine raw scores from different retrievers without normalization.
- Do not tune weights using a few anecdotal queries.
- Do not pass untrusted field names or query structures directly to search backends.
Frequently Asked Questions
What is hybrid search?
Hybrid search combines semantic vector retrieval with keyword retrieval to improve coverage across different query types.
When should I use reciprocal rank fusion?
Use it when retrievers produce scores on different scales or when you need a strong baseline with limited tuning.
Does this skill support PostgreSQL?
Yes. It includes patterns for pgvector, PostgreSQL full-text search, indexing, fusion, and optional reranking.
Does this skill support Elasticsearch?
Yes. It covers dense vector retrieval, BM25 matching, filters, and Elasticsearch reciprocal rank fusion.
How should I evaluate search quality?
Use labeled queries and measure ranking metrics, recall, latency, and performance across meaningful query segments.
Are the examples ready for production?
No. Adapt schemas, validate filters, test dependencies, review security, and benchmark behavior in your target environment.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/hybrid-search-implementationRef
88a8e9a07f4c54ab105c1c41b6267c287146b07b
Maintenance freshness
7/26/2026
Usage
8 downloads · 111 views
File structure
More from sickn33
View all📦
prompt-engineering-patterns
85Improve LLM Prompts With Proven Patterns
Inconsistent prompts waste time and make AI outputs hard to trust. This skill guides prompt design with reusable patterns, examples, evaluation steps, and optimization workflows.
Productivity View
📦
senior-fullstack
82Build Fullstack Apps with Senior Patterns
Teams need consistent setup, architecture, and review guidance for modern web applications. This skill provides scaffolders, workflow references, and quality prompts for React, Next.js, Node.js, GraphQL, and PostgreSQL projects.
Coding & Development View
📦
senior-architect
81Design Scalable Software Architectures
Architecture decisions are hard to compare across web, mobile, backend, and cloud systems. This skill provides structured guides and local scaffold scripts for reports, dependency review, and trade-off documentation.
Coding & Development View
📦
prompt-engineer
81Optimize AI Prompts With Prompt Engineer
Writing clear prompts is hard when goals are vague or complex. This skill turns rough requests into structured prompts using proven prompt frameworks.
Productivity View
📦
product-manager-toolkit
80Prioritize Product Work With Research Insights
Product teams need faster ways to rank features, synthesize interviews, and document decisions. This skill provides RICE scoring, interview analysis, and PRD templates for structured planning.
Productivity View
Related Skills
View all📦
advanced-agentdb-vector-search-implementation
79Build Advanced AgentDB Vector Search
by DNYoussef
Distributed vector search needs careful replication, metrics, monitoring, and deployment planning. This skill gives a phased AgentDB workflow for scalable hybrid search systems.
Data & Analytics View
📦
agentdb-vector-search-optimization
80Optimize AgentDB Vector Search
by DNYoussef
Large vector databases can become slow, memory-heavy, and hard to benchmark. This skill provides a structured AgentDB optimization workflow for quantization, HNSW indexing, caching, and validation.
Data & Analytics View
📦
agentdb-semantic-vector-search
80Build AgentDB Semantic Search
by DNYoussef
Teams need relevant document retrieval for RAG and knowledge bases. This skill guides AgentDB setup, embedding storage, indexing, API search, and relevance tuning.
Data & Analytics View
📦
embedding-strategies
79Choose Better Embedding Strategies
by wshobson
Embedding choices affect retrieval quality, latency, and cost. This skill helps teams select models, chunk content, and evaluate search results for RAG systems.
Data & Analytics View
📦
agentdb-vector-search
78Build AgentDB Vector Search
by DNYoussef
Teams need fast semantic retrieval for documents, RAG systems, and knowledge bases. This skill gives AgentDB setup, search patterns, and tuning guidance.
Data & Analytics View
📦
vector-index-tuning
77Optimize Vector Search Indexes
by wshobson
Vector search systems often trade recall, latency, and memory without clear tuning guidance. This skill provides parameter templates, quantization choices, and monitoring patterns for production indexes.
Data & Analytics View