Skill UI

A specialized skill for architecting, building, and productionizing complex Agent Development Kit (ADK) agents and multi-agent systems. This process emphasizes best practices, including clean module structure, robust tool integration, comprehensive regression testing, and deployment automation for reliable, shippable AI solutions. Use this when requiring the creation of structured, enterprise-grade agent code.

Provision production-grade Vertex AI ADK Agent Engine infrastructure with Terraform, covering Agent Engine runtime, code execution sandbox, Memory Bank, VPC Service Controls, and secure IAM setup for multi-agent deployments.

AFL++ is an advanced fork of AFL that runs multi-core campaigns, supports diverse mutations, and targets large C/C++ codebases with stable tooling via Docker or host builds.

Utilizes the Azure AI Projects SDK's ImageBasedHostedAgentDefinition to build robust, container-based AI agents deployed in Azure AI Foundry. This workflow is essential for creating production-grade agents that require custom logic, precise resource allocation (CPU/Memory), and integration of advanced tools like code interpreters.

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

Analyzes project dependencies via the dependency-checker plugin to flag security vulnerabilities, outdated packages, and license conflicts before deployment; run it when you need insight on risky npm, pip, composer, gem, or go modules.

Analyzes project dependencies via the dependency-checker plugin to flag security vulnerabilities, outdated packages, and license conflicts before deployment; run it when you need insight on risky npm, pip, composer, gem, or go modules.

Guides API developers through setting up mock services, recommending best practices, and generating production-ready code and configurations for REST, GraphQL, OpenAPI, and authentication scenarios.

Configures a robust CI/CD pipeline for Apollo.io integrations using GitHub Actions. It implements multi-stage testing, including unit tests (using MSW mocks for zero API calls), live integration tests (gated to main branch), and secret scanning. Ensures reliable and safe deployment practices for SaaS applications.

This comprehensive tool provides an automated, multi-stage checklist for deploying Apollo.io integrations to a production environment. It validates crucial aspects including secure authentication (API keys, headers), system resilience (rate limiting, retry logic), observability (PII redaction, structured logging), and financial controls (credit tracking). Use this checklist before going live or when performing production system audits to ensure full compliance and stability.

Argocd App Deployer is an auto-activating assistant for advanced DevOps operators working with Argo CD deployments. Mention “argocd app deployer” or request deployment best practices to get step-by-step recommendations, production-ready configs, validation, and guidance spanning Kubernetes, Terraform, Helm, monitoring, and IaC patterns.