Vibe Coding Discoverskills
View on GitHubAgent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go, .NET, Java
Installable agent skills that encode Qdrant vector-search engineering knowledge: scaling, sizing, quantization, multitenancy, hybrid search, model migration, upgrades and monitoring. Skills give 'when/why' guidance plus pointers into Qdrant docs for Claude Code, Cursor, Codex and other coding agents.
Use Cases
Diagnose slow vector search on large collectionsDecide horizontal vs vertical scaling for QdrantTune quantization, sharding and indexing performanceFix irrelevant search results and set up hybrid searchMigrate embedding models with zero downtimePlan safe Qdrant version upgradesIsolate multiple tenants via payload partitioningSize RAM, disk, CPU and node count before deploymentMonitor metrics, health checks and optimizer issuesGenerate Qdrant SDK snippets in Python, TypeScript, Rust, Go, .NET, JavaChoose between local, self-hosted, cloud and hybrid deploymentMove data from another vector DB with the migration tool
Built With
- Language
- Python
- Frameworks
- Claude Code · Cursor · OpenAI Codex · OpenCode · Pi
Tags
agent-skills · vector-search · qdrant · embeddings · hybrid-search · quantization · scaling · multitenancy · monitoring · vector-database · search-quality · deployment · claude-code · cursor · codex · rag