Skill UI

This skill orchestrates the entire development lifecycle for approved software tasks. It operates in autonomous or manual modes, rigorously applying Test-Driven Development (TDD). It automatically gathers all necessary context (specs, requirements, design, and historical learnings), validates the repository state, and dispatches specialized sub-agents to implement tasks one by one, ensuring high code quality, adherence to design, and process traceability.

This utility analyzes a codebase (product, technology, and structure) to automatically extract core architectural patterns, guiding principles, and engineering standards. It manages a persistent 'Steering' repository, performing initial bootstrapping and continuous synchronization. It detects 'code drift' when the code deviates from established patterns and suggests additive updates, ensuring project documentation remains a comprehensive source of truth.

This comprehensive guide provides step-by-step instructions for deploying applications integrated with Klaviyo across major modern cloud environments, including Vercel, Fly.io, and Google Cloud Run. It covers essential DevSecOps practices, such as managing platform-specific secrets (API keys, webhook signing secrets), configuring middleware for secure webhook validation (HMAC), and implementing robust health checks to ensure a stable, production-ready deployment.

This reference architecture provides a production-ready, layered structure for integrating external applications with Klaviyo. It outlines best practices for building robust marketing automation systems, including profile synchronization (bidirectional sync), event-driven webhook handlers, background job processing, and implementing middleware like rate limiters. Use this when designing new data pipelines, reviewing project structure, or establishing standardized data synchronization standards for email/SMS campaigns.

A comprehensive index of 46 production-grade test automation skills, covering E2E, unit, mobile, BDD, and cloud testing across 15+ languages. This skill equips AI coding assistants to act as senior QA architects, generating structured, real-world test code for major frameworks like Playwright, Selenium, Cypress, and Jest. It supports project setup, CI/CD integration, and cross-platform debugging.

This guide provides advanced patterns for composing robust, multi-step Large Language Model (LLM) workflows using LangChain 1.0 core runnables. Learn to implement parallel retrievals (RunnableParallel), conditional routing (RunnableBranch), and state management. It also addresses common pitfalls like silent KeyErrors due to mismatched dictionary shapes, providing solutions using debug probes and type annotations for reliable RAG, classification, and extraction pipelines.

This guide teaches how to build robust, production-grade ReAct agents using LangGraph 1.0's `create_react_agent`. It specifically addresses common agent failure modes, such as preventing infinite loops (recursion caps) and ensuring proper error propagation. Learn to define typed tools using Pydantic, manage state with checkpointers, and set precise recursion limits. Perfect for users migrating from legacy AgentExecutor or building reliable, multi-step tool-calling workflows.

This skill provides a comprehensive guide to building robust LangGraph 1.0 StateGraphs in Python. It covers defining typed TypedDict states with reducers, writing deterministic nodes, and defensively wiring conditional edges. It addresses common pitfalls such as silent graph termination, incorrect state updates using reducers, proper management of recursion limits, and handling evolving checkpoint schemas for reliable LLM workflow orchestration.

A comprehensive guide to ensuring robust state management for LangGraph agents. This skill addresses common checkpointing pitfalls (like missing thread_id or non-JSON serializable state) and details best practices for implementing reliable state persistence using MemorySaver or PostgresSaver. It covers thread discipline, schema migration, and time-travel debugging for production-ready applications.

This skill defines canonical, ordered middleware patterns for LangChain and LangGraph agents. It addresses critical production-level LLM deployment issues, including PII leakage across tenants, token budget overconsumption, prompt injection defense (Guardrails), and cache key corruption. Use this to build secure, scalable, and cost-aware AI applications.

A comprehensive guide to advanced, production-ready LLM inference using LangChain 1.0. This skill addresses common pitfalls and bugs when integrating multiple providers (Claude, GPT-4o, Gemini), covering issues like structured output reliability, correct streaming token accounting, and robust handling of complex AIMessage content blocks. It teaches best practices for model initialization, provider routing, and version-safe code implementation.

This guide provides comprehensive, structured SDK patterns for interacting with the Linktree REST API. It addresses critical development challenges such as managing rate limits (429), handling authentication failures (401), and ensuring data consistency through typed objects. Patterns include a thread-safe singleton client, robust error wrappers with exponential backoff, fluent request builders for pagination, and middleware for logging and authorization, making API integration reliable and scalable.