Plugins
5 pluginscurated
Project Verification Pipeline
For developers running comprehensive verification pipelines for Laravel or Quarkus projects before PRs or releases.
4 skills · plugin
curated
DotNet Test Migration to MTP
Migrate .NET test projects from VSTest to MTP, updating project files, CLI, and CI/CD pipelines.
3 skills · plugin
curated
FP-TS Backend Toolkit
For Node.js/Deno developers building type-safe backends with fp-ts, covering async pipelines and service composition.
3 skills · plugin
@dotnet
Dotnet AI
AI and ML skills for .NET: technology selection, LLM integration, agentic workflows, RAG pipelines, MCP, and classic ML with ML.NET.
5 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
Results for “pipelines”
8 skills
antigravity AI Ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines with observability and security.
42.4k
nvidia Nemo Data Designer Plugin
Build synthetic datasets and data generation pipelines using the Data Designer library.
2.2k · bundle
nimoqup046-collab LLM Ops
Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.
2
More results
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
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
deep-chavda AI Engineering Standards
Enforces production-grade Python and AI engineering standards for FastAPI, LangChain/LangGraph, RAG pipelines, and LLM integrations, covering type safety, error handling, testing, and security.