Skill UI

A comprehensive CI/CD pipeline designed for LangChain applications. It automates quality assurance through fast, mocked unit tests and slower, more accurate integration tests utilizing real LLMs (e.g., OpenAI). It specifically validates the entire RAG pipeline and integrates tracing with LangSmith, ensuring robust deployment quality.

This blueprint provides a scalable, production-grade reference architecture for advanced LLM applications using LangChain. It guides the implementation of layered design, provider abstraction (allowing seamless switching between LLMs like OpenAI and Anthropic), dynamic chain registry, robust RAG pipelines, and complex multi-agent orchestration. Ideal for building enterprise-level, maintainable, and highly flexible AI systems.

Build and configure Laravel 10+ apps using Eloquent models, Sanctum auth, Horizon queues, Livewire interfaces, and REST APIs while ensuring PSR-12, dependency injection, eager loading, and Pest/PHPUnit coverage above 85%.

LibFuzzer is LLVM’s in-process coverage-guided fuzzer for C/C++ projects compiled with Clang, providing a quick harness-based setup that can later transition to more advanced fuzzers while maintaining reproducible, high-throughput testing.

This skill provides comprehensive reference architectures for integrating Lindy AI agents into production applications. It covers various complex patterns, including simple webhook callbacks, sophisticated event-driven pipelines, multi-agent delegation systems, scheduled workflows, and RAG-powered customer chat interfaces. Essential for designing and building robust, scalable, and reliable AI-powered systems.

This guide details secure methods for connecting external applications to the Linear API. It covers essential security practices, including secure storage of API keys using environment variables, implementing OAuth 2.0 with PKCE for robust authentication, managing token refresh cycles, and verifying webhook signatures using HMAC-SHA256. Essential for building production-grade, secure integrations.

A comprehensive guide to integrating Lokalise into Continuous Integration/Continuous Deployment (CI/CD) workflows using GitHub Actions. This setup automates the entire translation lifecycle: uploading source strings on code pushes, downloading the latest translations during builds, and running PR checks to ensure translation coverage across all required locales. It eliminates manual file management, keeping code, translations, and CI pipelines perfectly synchronized.

This comprehensive script suite provides a structured, multi-step audit for internationalization (i18n) integrations using Lokalise. It automates crucial checks, including verifying translation coverage across all required locales, detecting key synchronization issues (missing or orphaned keys) between the source code and the Translation Management System (TMS), and validating the format of localized files. Use this utility before any major production deployment or go-live phase to ensure localization readiness.

Helps manage container registries like AWS ECR, Google GCR, and Harbor by generating configuration code, guiding access controls, and advising best practices for secure, production-ready image storage and deployment automation.

This guide details the process of building Model Context Protocol (MCP) servers. These servers are crucial for enabling Large Language Models (LLMs) to interact with external APIs and services, facilitating complex, real-world task completion. It provides comprehensive coverage on architectural planning, choosing appropriate languages (Python, TypeScript, C#), and selecting transport mechanisms (HTTP, stdio) for robust integration within enterprise ecosystems like Azure and Foundry.

This guide provides comprehensive instructions for installing and configuring the official Mistral AI SDK (for both Python and Node.js). It covers setting up API keys via environment variables, verifying the connection, and implementing secure credential storage using Secret Managers (GCP/AWS) for production environments. Ideal for starting any new Mistral AI project.

This guide provides a production-ready, layered architecture pattern for integrating Mistral AI into TypeScript/Node.js projects. It implements best practices including singleton client management, Zod-validated configuration, custom error handling, caching mechanisms, and robust service layer design (e.g., RAG pipelines). Use this when setting up new Mistral AI applications or needing standardized, reliable project structure.