Plugins
3 pluginscurated
Build RAG Pipeline with Pinecone
Build a production RAG pipeline and persistent agent memory using Pinecone as the vector database backend.
6 skills · plugin
curated
Deploy Azure ML Pipeline
Manage Azure Machine Learning resources including workspaces, jobs, models, data, compute, and pipelines using the SDK v2 for Python.
3 skills · plugin
@alirezarezvani
Engineering
37 advanced engineering skills: agent designer, agent workflow designer, RAG architect, database designer + schema designer + SQL assistant, migration architect, observability designer, dependency auditor, changelog generator (with semantic version bumper and hotfix/rollback procedures), API design reviewer, API test suite builder, CI/CD pipeline builder, MCP server builder, skill security auditor
33 skills · plugin
Results for “data-pipeline”
9 skills
alirezarezvani RAG Architect
Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.
20.4k · bundle
github Pinecone RAG
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.
36.2k
nimoqup046-collab LLM Ops
Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.
2
jorcan LLM Ops
Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.
0 · bundle
More results
lucaspmarie-a11y LLM Ops
Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.
5
phoroth LLM Ops
Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.
3
antigravity LLM Ops
Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.
42.4k
majiayu000 RAG
Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.
567 · bundle
jeffallan RAG Architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.
10.4k · bundle