when-building-semantic-search-use-agentdb-vector-search
This skill guides developers through building a semantic search system using AgentDB. It provides a 5-phase implementation plan with TypeScript examples, covering vector database setup, document embedding, search indexing, API creation, and optimization. The skill includes concrete success metrics like retrieval accuracy and latency targets.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install dnyoussef-ai-chrome-extension-when-building-semantic-search-use-agentdb-vector-search
Repository
Skill path: .claude/skills/agentdb/when-building-semantic-search-use-agentdb-vector-search
This skill guides developers through building a semantic search system using AgentDB. It provides a 5-phase implementation plan with TypeScript examples, covering vector database setup, document embedding, search indexing, API creation, and optimization. The skill includes concrete success metrics like retrieval accuracy and latency targets.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Data / AI, Backend.
Target audience: AI/ML teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: DNYoussef.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install when-building-semantic-search-use-agentdb-vector-search into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/DNYoussef/ai-chrome-extension before adding when-building-semantic-search-use-agentdb-vector-search to shared team environments
- Use when-building-semantic-search-use-agentdb-vector-search for ai/ml workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.