Agent Skills

DSPy — Declarative LM Programming

opengauss

Use DSPy to build declarative, modular LM pipelines, optimize prompts automatically, and assemble reliable RAG/agent systems with structured signatures and opti

Terradev — Cross‑Cloud GPU Provisioning

Terradev

Provision and manage GPUs across clouds, create GPU Kubernetes clusters, deploy inference endpoints, and burst local compute to cloud with BYOAPI credential saf

RAG Implementation Patterns

claude-code-toolkit

Practical guide to building production Retrieval-Augmented Generation (RAG) systems: vector DB selection, chunking strategies, embedding model choices, retrieva

MindsDB

application-skills

Integrate and interact with MindsDB via the Membrane CLI: manage connections, list and run actions, and automate model predictions against your database.

Cortex - ML Pipeline Builder

claude-code-plugins-plus-skills

Guided ML pipeline builder that walks an agent through data validation, feature engineering, training, evaluation, and serving endpoints for classification or r

Model Deployment (SageMaker & Bedrock)

agent-plugins

Generates deployment code and a Jupyter notebook to deploy LoRA fine-tuned Nova or OSS models (from SageMaker Serverless Model Customization) to SageMaker endpo

Cortex — ML Pipeline Builder

tonone

Build a reproducible ML training-to-serving pipeline: data validation, feature engineering, training, evaluation, and a serving endpoint.

Replicate Model Runner

suna

Run, compare and manage AI model inferences via the Replicate API for image, video, and audio generation and other ML inference tasks.

CV Model Training

cv-train-stack

Review, run, validate and audit computer vision model training with checks for dataset quality, preprocessing consistency, augmentation, and deployment validati

Weights & Biases (wandb)

zorai

Integrate Weights & Biases for ML experiment tracking: log metrics, hyperparameters, checkpoints, run sweeps and view collaborative dashboards.

Track ML Experiments (MLflow)

agent-almanac

Set up MLflow experiment tracking: server, autologging, artifact storage, run comparison and lifecycle management for reproducible ML workflows.

MLOps Engineer

claude-skills

Provides senior MLOps guidance: build and validate ML pipelines, deploy and monitor models in production, set up feature stores, and automate CI/CD for models.

Scout

DeepScientist

Frame ambiguous research or engineering tasks: clarify goals, metrics, datasets, and a justified baseline so work can proceed with confidence.