Skill UI

Gastown is a robust multi-agent orchestration system designed for Claude Code. It enables the simultaneous execution of multiple AI agents on complex projects. It manages the entire development lifecycle, from setting up workspaces and tracking work items (beads) to managing agent lifecycles (polecats/crew) and merging final code (Refinery). Use it when coordinating complex, parallel AI tasks.

This skill orchestrates a comprehensive, multi-stage Git development workflow, simulating the process from code commit to PR preparation. It leverages specialized AI agents (code reviewers, test automators, prompt engineers) to systematically enforce best practices. Key functions include code quality assessment, breaking change analysis, running unit/integration/E2E tests, and generating Conventional Commit messages, ensuring code readiness and high quality before merging.

Guides using Rube MCP and Composio’s Hystruct toolkit to automate Hystruct workflows, covering tool discovery, connection checks, execution patterns, and schema compliance for reliable task orchestration.

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.

Guides data-engineering teams through configuring incremental loads in pipelines, covering best practices, orchestration tips, example code, and output validation for streaming or batch workflows.

Automates Kafka stream processor workflows within data pipelines, offering step-by-step guidance, best practices, production-ready code and configurations, and validation to support ETL, data transformation, workflow orchestration, and streaming data processing.

An expert guide for designing, implementing, and optimizing large-scale, secure, and highly available cloud-native infrastructure using Kubernetes. Covers advanced topics including GitOps workflows (ArgoCD/Flux), service mesh architecture (Istio), multi-cluster management, Policy as Code (OPA), and infrastructure automation with Terraform. Ideal for platform engineers building enterprise-grade container orchestration solutions.

Automates Luigi task generation for data pipelines, offering guided best practices, code and configuration outputs, and validation to streamline ETL, transformation, orchestration, and streaming workflows.

This skill guides the design and implementation of a complete, production-grade Machine Learning pipeline. It employs a multi-agent orchestration workflow, systematically covering all stages of the ML lifecycle: data ingestion and quality assurance, feature engineering, model training, optimization, and robust deployment (MLOps). It ensures built-in reproducibility, monitoring, and scalability.

This skill provides comprehensive guidance for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, from initial data ingestion and rigorous feature engineering to model training, advanced validation (A/B testing, performance drift), and robust deployment strategies (Canary, Blue-green). It enables the design and orchestration of scalable, reproducible, and observable ML systems using tools like Airflow and Kubeflow.

Guide for automating Ncscale operations through Composio’s Rube MCP, covering connection prerequisites, mandatory schema-aware tool discovery, execution steps, and safety notes for reliable workflow orchestration.

Automates Openrouter workflows using Rube MCP’s Composio toolkit, walking through tool discovery, connection checks, and schema-compliant execution for reliable orchestration.