hybrid-search-implementation

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

5 stars

Best use case

hybrid-search-implementation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

Teams using hybrid-search-implementation should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/hybrid-search-implementation/SKILL.md --create-dirs "https://raw.githubusercontent.com/FrancoStino/opencode-skills-collection/main/bundled-skills/hybrid-search-implementation/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/hybrid-search-implementation/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How hybrid-search-implementation Compares

Feature / Agenthybrid-search-implementationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Hybrid Search Implementation

Patterns for combining vector similarity and keyword-based search.

## Use this skill when

- Building RAG systems with improved recall
- Combining semantic understanding with exact matching
- Handling queries with specific terms (names, codes)
- Improving search for domain-specific vocabulary
- When pure vector search misses keyword matches

## Do not use this skill when

- The task is unrelated to hybrid search implementation
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## Resources

- `resources/implementation-playbook.md` for detailed patterns and examples.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Related Skills

xvary-stock-research

5
from FrancoStino/opencode-skills-collection

Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).

slo-implementation

5
from FrancoStino/opencode-skills-collection

Framework for defining and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and error budgets.

similarity-search-patterns

5
from FrancoStino/opencode-skills-collection

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

seo-aeo-keyword-research

5
from FrancoStino/opencode-skills-collection

Researches and prioritises SEO keywords with AEO question queries, difficulty tiers, cannibalization checks, and a content map. Activate when the user wants to find keywords, research search terms, or build a keyword strategy.

search-specialist

5
from FrancoStino/opencode-skills-collection

Expert web researcher using advanced search techniques and

rag-implementation

5
from FrancoStino/opencode-skills-collection

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

not-human-search-mcp

5
from FrancoStino/opencode-skills-collection

Search AI-ready websites, inspect indexed site details, verify MCP endpoints, and discover tools and APIs using the Not Human Search MCP server

hybrid-cloud-networking

5
from FrancoStino/opencode-skills-collection

Configure secure, high-performance connectivity between on-premises and cloud environments using VPN, Direct Connect, and ExpressRoute.

hybrid-cloud-architect

5
from FrancoStino/opencode-skills-collection

Expert hybrid cloud architect specializing in complex multi-cloud solutions across AWS/Azure/GCP and private clouds (OpenStack/VMware).

hig-components-search

5
from FrancoStino/opencode-skills-collection

Apple HIG guidance for navigation-related components including search fields, page controls, and path controls.

exa-search

5
from FrancoStino/opencode-skills-collection

Semantic search, similar content discovery, and structured research using Exa API. Use when you need semantic/embeddings-based search, finding similar content, or searching by category (company, people, research papers, etc.).

deep-research

5
from FrancoStino/opencode-skills-collection

Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.