Agent Skills Hub

dlt-skill

Create, manage, and debug dlt (data load tool) pipelines for ingesting data from APIs, databases, and custom sources into destinations like DuckDB, BigQuery, and Snowflake.

workflow-execute-plans

Execute implementation plans in small, verifiable batches with pause-for-feedback checkpoints to prevent drift and ensure code quality.

shift-right-testing

Production-grade testing strategy implementing feature flags, canary releases, synthetic monitoring, and chaos engineering for continuous reliability in live environments.

n8n-workflow-testing-fundamentals

Comprehensive n8n workflow testing framework for lifecycle validation, node-to-node data flow, error handling, and performance benchmarking in automated environments.

ring:dev-sre

Gate 2 development cycle skill that validates observability implementation, including structured logging, OpenTelemetry tracing, and instrumentation coverage, without modifying code.

connections

Lightweight MCP (Model Context Protocol) connection handler supporting stdio, SSE, and streamable HTTP transports for seamless server integration.

mcp-server-creator

Generate production-ready Stigmer McpServer YAML configurations for integrating external tools into your AI agents.

gitops

Automate Kubernetes GitOps workflows with ArgoCD, Helm, and Kustomize. Manage multi-environment deployments, infrastructure as code, and CI/CD pipelines efficiently.

agentic-workflows

Build production-grade AI agents using LangGraph, Anthropic/OpenAI/vLLM, and structured outputs. Features streaming, A2A protocol, Pydantic validation, vector memory, and guardrails for resilient, multi-agent workflows.

venv-manager

Automated Python virtual environment manager for project isolation, dependency management, and lifecycle validation.

tool-creator

Meta-skill for building, managing, and hot-reloading AnimaWorks Python external tools, including dispatchers, credential management, and permissions.

milady-development

Self-modify your Milady agent by managing plugins. Edit code, rebuild, and restart the runtime to develop new capabilities or improve agent workflows locally.