# 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. Use when this capability is needed. - Skill: `tomevault-io/hybrid-search-implementation-2` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/hybrid-search-implementation-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/hybrid-search-implementation-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/hybrid-search-implementation-2 --- # 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. --- ## AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Memory-First Protocol Retrieve prior design decisions (color palettes, typography, spacing scales) to maintain visual consistency across sessions. Cache generated design tokens. ```bash # Check for prior frontend/design context before starting python3 execution/memory_manager.py auto --query "design system decisions and component patterns for Hybrid Search Implementation" ``` ### Storing Results After completing work, store frontend/design decisions for future sessions: ```bash python3 execution/memory_manager.py store \ --content "Design system: adopted 8px grid, Inter font family, HSL color tokens with dark mode support" \ --type decision --project \ --tags hybrid-search-implementation frontend ``` ### Multi-Agent Collaboration Share design decisions with backend agents (API contract changes) and QA agents (visual regression baselines). ```bash python3 execution/cross_agent_context.py store \ --agent "" \ --action "Implemented UI components — new design system with accessibility compliance (WCAG 2.1 AA)" \ --project ``` ### Design Memory Persistence Store design system tokens and component decisions in Qdrant so any agent on any platform (Claude, Gemini, Cursor) can retrieve and apply consistent styling. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/techwavedev) — claim your Tome and manage your conversions.