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Covers designing agent tools from schema creation through validation and error handling, emphasizing descriptions that keep LLMs accurate and reusable via MCP standards.

A comprehensive guide to building enterprise-grade AI applications using Firebase Genkit. This skill covers the full development lifecycle, including implementing advanced RAG systems, multi-step tool-calling agents, and complex workflows across Node.js, Python, and Go. It teaches best practices for schema validation (Zod/Pydantic), robust error handling, OpenTelemetry tracing, and deploying reliably to Firebase Functions or Cloud Run.

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.

This guide outlines the comprehensive reference architecture for building advanced, AI-powered search and research applications, modeled after Perplexity. It covers three core tiers: a searchable service with model routing and caching, a citation pipeline for validation and rendering, and an orchestrator for multi-query research synthesis. Use this when designing complex, production-grade, search-augmented LLM integrations or reviewing advanced system patterns.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.

A multi-layered defense system designed to detect sophisticated prompt injection attacks targeting LLM-powered applications. It combines regex pattern matching for known signatures, heuristic scoring for structural anomalies, and advanced DeBERTa-based classification. Ideal for building robust input validation layers, securing RAG pipelines, and performing proactive AI security scanning.

This tool generates production-grade, spec-compliant agent definition files (.md) adhering to the Anthropic 2026 schema. It supports both the creation of new custom subagents and the validation of existing agent files against marketplace compliance rules. Use it when building complex agent architectures, reviewing agent quality, or ensuring agents meet high industry standards.

This comprehensive skill provides multi-layered guardrails for integrating with LLMs like Claude. It enforces security and compliance by implementing input validation (PII detection, length checks), system prompt restriction (scope control, safety rules), output filtering (preventing sensitive data leakage), cost governance, and model access policies. Ideal for building secure, robust, and governed AI applications.