Skill UI

This tool enforces four rigorous coding principles, inspired by Andrej Karpathy's insights on LLM pitfalls. It guides developers to surface assumptions, prioritize simplicity, make surgical changes, and define verifiable goals. Use it when reviewing code, checking diffs, or before committing to ensure high code quality and prevent over-engineering.

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.

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 checklist for deploying Miro REST API v2 integrations into a production environment. This guide covers critical best practices including OAuth scope minimization, robust error handling, rate limit management, implementing circuit breakers, health checking, and defining clear rollback procedures. Use this when moving any Miro-integrated application to a live, customer-facing state.

This skill provides a comprehensive, production-grade reference architecture for building robust Notion integrations. It defines a multi-layered structure, including client singletons, repository patterns, service orchestration, and caching mechanisms. It is essential for migrating ad-hoc API calls to scalable, event-driven, and highly reliable enterprise applications that incorporate rate limiting, sophisticated error handling, and comprehensive testing strategies.

This skill provides a comprehensive Python guide to building a production-ready Virtual Cloud Network (VCN) on Oracle Cloud Infrastructure (OCI). It details the entire process from scratch, including creating the VCN, setting up Internet and NAT Gateways, configuring route tables, and defining Network Security Groups (NSGs). This is essential for engineers needing to provision complex, secure, and highly functional network architectures for development or production use.

This skill provides a comprehensive guide for safely upgrading the Oracle Cloud Infrastructure (OCI) Python SDK and Terraform provider. It addresses known breaking changes, schema modifications, and version incompatibility issues common in OCI tools. By implementing version pinning, pre-upgrade testing, and structured rollback procedures, users can ensure a stable and reliable migration process for both code and infrastructure definitions.

This workflow provides comprehensive SDK access to the Palantir Foundry Ontology. It enables users to query, filter, link, and mutate Ontology objects, and execute defined business actions. Use this skill for building sophisticated, data-driven applications that require deep interaction with structured enterprise data, including cross-object navigation and advanced server-side aggregation.

This guide teaches best practices for configuring robust, enterprise-grade Role-Based Access Control (RBAC) within Palantir Foundry. Learn how to define project roles (Viewer, Editor, Owner), manage organization-level groups to enforce least privilege, establish secure service user accounts for external integrations, and utilize marking-based data classification to ensure data governance and security across large datasets.

This skill automates the critical submittal workflow within the Procore construction management platform. Users can programmatically create new submittals, defining necessary specifications, assigning reviewers, and setting due dates. It further supports updating the submittal status (e.g., approving or rejecting) and listing filtered results, ensuring systematic tracking and smooth management of the entire project review cycle.

This guide provides comprehensive guidelines for performing vertical scaling on Qdrant instances. Vertical scaling involves upgrading the computational resources (CPU, RAM, or disk) of existing nodes rather than adding new nodes. It is the recommended first step when single nodes experience resource exhaustion (e.g., RAM usage > 80%, CPU saturation). The guide covers sizing guidelines, best practices for cloud deployment, and clearly defines the thresholds when horizontal scaling becomes necessary.