Skill UI

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.

Guides multi-step deliveries by linking Antigravity skills into workflows for SaaS MVP launches, security audits, AI agent systems, and browser QA, recommending best-fit playbooks and verifying each artifact.

Guides multi-step deliveries by linking Antigravity skills into workflows for SaaS MVP launches, security audits, AI agent systems, and browser QA, recommending best-fit playbooks and verifying each artifact.

This skill guides users on fine-tuning CodeRabbit's review quality. It teaches the AI agent team preferences through PR feedback, integrates custom code guidelines (e.g., security policies), allows customization of the review tone, and implements path filters to drastically reduce false positives and noise. This ensures that all code feedback is highly relevant, actionable, and aligns with specific team standards.

Orchestrates an exhaustive, multi-dimensional code review process by coordinating specialized agents (e.g., security, performance, quality, architecture). This workflow covers code quality, architectural integrity, security vulnerabilities (OWASP Top 10), performance bottlenecks, and test coverage, providing consolidated, actionable feedback and remediation guidance.

A sophisticated AI system that coordinates multiple specialized agents (e.g., Security Auditors, Performance Analysts, Architecture Specialists) to perform comprehensive, multi-perspective code assessments. It analyzes code context, manages state, and synthesizes conflicting insights to provide deep, holistic reviews for complex software projects.

Manages Firebase/Firestore CRUD, queries, batch jobs, and security for applications while extending to A2A agent messaging, MCP server integration, and Cloud Run coordination for advanced workflows.

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

Validates Vertex AI Agent Engine GitHub Actions workflows, enforcing Workload Identity Federation, least-privilege IAM, OIDC permissions, and security scanning so GCP deployments stay automated and secure.

This comprehensive checklist guides the deployment of AI agents into a live production environment. It covers critical areas including security best practices (API key management, SSO), robust error handling, monitoring setup, operational runbooks, and compliance adherence (HIPAA, GDPR). Use this when validating an agent for go-live to ensure maximum reliability and stability.

This comprehensive guide outlines essential security best practices for implementing and using Lindy AI agents. It covers crucial topics such as robust API key management, webhook signature verification, strict agent permission scoping, and secure data handling protocols. Use this resource when auditing agent activity, configuring enterprise security features (SSO/SCIM), or ensuring compliance and minimizing the attack surface area of autonomous AI systems.