Build and deploy production-ready generative AI agents using Vertex AI, Gemini models, and Google Cloud infrastructure with RAG, function calling, and multi-modal capabilities. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Execute Deepgram incident response procedures for production issues. Use when handling Deepgram outages, debugging production failures, or responding to service degradation. Trigger with phrases like "deepgram incident", "deepgram outage", "deepgram production issue", "deepgram down", "deepgram emergency".
Build sklearn pipeline builder operations. Auto-activating skill for ML Training. Triggers on: sklearn pipeline builder, sklearn pipeline builder Part of the ML Training skill category. Use when working with sklearn pipeline builder functionality. Trigger with phrases like "sklearn pipeline builder", "sklearn builder", "sklearn".
Execute pre-launch production readiness checklist for Kling AI. Use when preparing to deploy video generation to production. Trigger with phrases like 'klingai production', 'kling ai go-live', 'klingai launch checklist', 'deploy klingai'.
Deploy vertex ai deployer operations. Auto-activating skill for ML Deployment. Triggers on: vertex ai deployer, vertex ai deployer Part of the ML Deployment skill category. Use when deploying applications or services. Trigger with phrases like "vertex ai deployer", "vertex deployer", "deploy vertex ai er".
Implement observability for Lindy AI integrations. Use when setting up monitoring, logging, tracing, or building dashboards for Lindy operations. Trigger with phrases like "lindy monitoring", "lindy observability", "lindy metrics", "lindy logging", "lindy tracing".
Configure Vast.ai local development with hot reload and testing. Use when setting up a development environment, configuring test workflows, or establishing a fast iteration cycle with Vast.ai. Trigger with phrases like "vastai dev setup", "vastai local development", "vastai dev environment", "develop with vastai".
Configure LangChain local development workflow with hot reload and testing. Use when setting up development environment, configuring test fixtures, or establishing a rapid iteration workflow for LangChain apps. Trigger with phrases like "langchain dev setup", "langchain local development", "langchain testing", "langchain development workflow".
Manage multi-agent orchestrator for Claude Code. Use when user mentions gastown, gas town, gt commands, bd commands, convoys, polecats, crew, rigs, slinging work, multi-agent coordination, beads, hooks, molecules, workflows, the witness, the mayor, the refinery, the deacon, dogs, escalation, or wants to run multiple AI agents on projects simultaneously. Handles installation, workspace setup, work tracking, agent lifecycle, crash recovery, and all gt/bd CLI operations. Trigger with phrases like "gas town", "gt sling", "fire up the engine".
This skill enables Claude to deploy machine learning models to production environments. It automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. Use this skill when the user requests to deploy a model, serve a model via an API, or put a trained model into a production environment. The skill is triggered by requests containing terms like "deploy model," "productionize model," "serve model," or "model deployment."
Data Augmentation Pipeline - Auto-activating skill for ML Training. Triggers on: data augmentation pipeline, data augmentation pipeline Part of the ML Training skill category.
This skill enables Claude to construct and evaluate classification models using provided datasets or specifications. It leverages the classification-model-builder plugin to automate model creation, optimization, and reporting. Use this skill when the user requests to "build a classifier", "create a classification model", "train a classification model", or needs help with supervised learning tasks involving labeled data. The skill ensures best practices are followed, including data validation, error handling, and performance metric reporting.
Dataset Loader Creator - Auto-activating skill for ML Training. Triggers on: dataset loader creator, dataset loader creator Part of the ML Training skill category.
Create vertex ai pipeline creator operations. Auto-activating skill for GCP Skills. Triggers on: vertex ai pipeline creator, vertex ai pipeline creator Part of the GCP Skills skill category. Use when working with vertex ai pipeline creator functionality. Trigger with phrases like "vertex ai pipeline creator", "vertex creator", "vertex".
This skill enables Claude to split datasets into training, validation, and testing sets. It is useful when preparing data for machine learning model development. Use this skill when the user requests to split a dataset, create train-test splits, or needs data partitioning for model training. The skill is triggered by terms like "split dataset," "train-test split," "validation set," or "data partitioning."
Mlflow Tracking Setup - Auto-activating skill for ML Training. Triggers on: mlflow tracking setup, mlflow tracking setup Part of the ML Training skill category.
This skill enables Claude to split datasets into training, validation, and testing sets. It is useful when preparing data for machine learning model development. Use this skill when the user requests to split a dataset, create train-test splits, or needs data partitioning for model training. The skill is triggered by terms like "split dataset," "train-test split," "validation set," or "data partitioning."
This skill enables Claude to split datasets into training, validation, and testing sets. It is useful when preparing data for machine learning model development. Use this skill when the user requests to split a dataset, create train-test splits, or needs data partitioning for model training. The skill is triggered by terms like "split dataset," "train-test split," "validation set," or "data partitioning."
This skill automates the setup of machine learning experiment tracking using tools like MLflow or Weights & Biases (W&B). It is triggered when the user requests to "track experiments", "setup experiment tracking", "initialize MLflow", or "integrate W&B". The skill configures the necessary environment, initializes the tracking server (if needed), and provides code snippets for logging experiment parameters, metrics, and artifacts. It helps ensure reproducibility and simplifies the comparison of different model runs.
Train Test Splitter - Auto-activating skill for ML Training. Triggers on: train test splitter, train test splitter Part of the ML Training skill category.
