Skill UI

A comprehensive guide for diagnosing and fixing common errors when interacting with the Linear API or SDK. Covers critical topics such as authentication failures (401), rate limiting (429), excessive query complexity, and handling null references. Essential for building robust and reliable software integrations.

A comprehensive guide and implementation using TypeScript to establish production-grade observability for Linear API integrations. This includes defining Prometheus metrics (tracking latency, error rates, rate limits, and webhooks), implementing structured logging (using pino), and setting up robust health check endpoints. It ensures full visibility into API performance and system health, critical for mission-critical SaaS applications.

This comprehensive checklist provides best practices and patterns for deploying Linear integrations to a production environment. It covers critical areas including robust authentication and secure secret management, error handling and system resilience (rate limiting, timeouts), performance optimization (caching, batching), and setting up full observability (health checks, structured logging). Use this when preparing for a go-live or auditing existing deployments.

This guide provides advanced techniques for managing API rate limits (429 errors) and complexity budgets when interacting with the Linear API. It covers reading rate limit headers, implementing exponential backoff with jitter for robust retries, controlling request bursts via token buckets, and optimizing GraphQL queries to minimize complexity scores. Use this when developing reliable integrations that must adhere to strict API quotas.

A comprehensive guide covering advanced patterns for the Linear SDK using TypeScript/JavaScript. Learn how to implement efficient cursor-based pagination, avoid N+1 query problems by batch-loading relations, perform complex filtering, and handle API calls with type-safe error management when integrating with the Linear GraphQL API.

An expert guide and framework for building production-grade, sophisticated AI agent systems. This skill specializes in utilizing the latest LangChain 0.1+ and LangGraph APIs to implement multi-agent orchestration, advanced Retrieval-Augmented Generation (RAG) pipelines (e.g., RAG Fusion, HyDE), and complex state management. It covers comprehensive topics from architectural design (Plan-and-Execute, ReAct Agents) to production deployment (FastAPI streaming, LangSmith observability, caching, and robust error handling).

Execute comprehensive load, stress, and soak tests to validate API performance, identify scaling bottlenecks, and establish throughput baselines. This tool simulates realistic, high-volume traffic patterns using industry-standard tools like k6 or Artillery, allowing users to define complex scenarios (e.g., e-commerce checkout flows, traffic spikes) and assert performance thresholds (p95 latency, error rates). It helps pinpoint resource limitations in databases, network layers, or compute resources, ensuring the API is ready for production scalability.

A comprehensive guide for diagnosing and fixing common Lokalise API errors and exceptions. This skill details the error response format and provides diagnostic cURL commands for specific HTTP status codes (401, 400, 404, 429, 413, 500, 503). Use it when encountering failed requests, debugging integration issues, or needing to verify API token validity and rate limit status.

This skill provides robust patterns for handling API rate limits and throttling, specifically demonstrated using the Lokalise API. It teaches how to implement request queuing (p-queue) with required spacing, automatic exponential backoff with jitter upon receiving 429 errors, and how to proactively monitor and utilize rate limit headers (X-RateLimit-*). Ideal for optimizing high-throughput, reliable API interactions and bulk operations.

This guide provides production-grade coding patterns for integrating Lokalise into TypeScript and Node.js applications. It covers crucial best practices such as client singleton implementation, robust cursor-based pagination for large datasets, structured error handling with retry logic, and efficient batch operations. Implementing these patterns ensures reliable, scalable, and maintainable API usage.

A comprehensive quick reference guide for troubleshooting and resolving common HTTP errors (4xx, 5xx) encountered when integrating with the MaintainX API. It provides detailed root causes, diagnostic commands (using curl/jq), and specific fixes for various status codes, such as 400 Bad Request, 401 Unauthorized, 429 Rate Limit, and 500 Internal Server Error, ensuring stable application integration.

This comprehensive checklist guides developers and DevOps engineers through the necessary steps for deploying MaintainX integrations to a production environment. It covers crucial areas such as authentication security, error resilience (retry/circuit breaking), data integrity (idempotency, pagination), observability setup (logging/metrics), and modern deployment best practices, ensuring a stable and reliable go-live process.