Skill UI

This guide provides comprehensive strategies for managing AI agent resource consumption and credit usage. It details optimizing task steps, setting trigger filters, implementing webhook rate limiting, and setting up budget alerts. Use this guide to ensure efficient, cost-effective AI operations and prevent unexpected billing overruns.

This guide provides advanced strategies for optimizing API usage with Linear, helping users stay within strict rate limits and minimize operational costs. It covers best practices like replacing polling with webhooks, implementing request coalescing, minimizing query complexity, and utilizing smart caching with tailored TTLs, ensuring maximum throughput efficiency.

A comprehensive runbook providing step-by-step procedures for managing production incidents related to Linear integrations. It guides users through diagnosing outages, checking API authentication, managing rate limits, and resolving webhook failures, ensuring a quick and effective response across various severities.

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 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 structured playbook for rapidly diagnosing and resolving production incidents related to Lokalise localization services. It guides users through essential quick diagnostics (API health, token validity, rate limits) and provides a decision tree for common failure modes, such as missing or stale translations, API outages, and upload failures, ensuring swift mitigation and postmortem readiness.

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.

A comprehensive client library pattern for interacting with rate-limited APIs. This solution implements exponential backoff, robust retry logic (handling 429/5xx errors), concurrent request queuing, and cursor-based pagination. It ensures high throughput and reliable data fetching without exceeding API limits.

This guide provides production-grade patterns for building robust integrations with the MaintainX REST API. It covers essential techniques such as creating type-safe clients using TypeScript, implementing reliable cursor-based pagination, handling API rate limits with exponential backoff retry logic, and performing efficient batch operations. Ideal for developers building scalable, resilient API wrappers.

A comprehensive guide for diagnosing and fixing common errors and exceptions encountered when using the Mistral AI API. Covers critical topics such as 401 Unauthorized, 429 Rate Limiting, invalid model IDs, context length limits, and server errors, providing actionable fixes and code snippets for developers.

This guide details how to implement Role-Based Access Control (RBAC) and comprehensive workspace management for Mistral AI at the enterprise level. It covers creating scoped API keys, setting resource limitations (rate limits, model access), enforcing spending budgets, key auditing, and automating key rotation. It is essential for large organizations requiring strict governance, security, and cost control when integrating Mistral AI into production systems.

This skill provides a comprehensive framework for configuring Mistral AI across distinct environments (development, staging, and production). It manages environment-specific secrets (using GCP, AWS, or Vault), defines critical parameters like default models, rate limits, and feature flags, ensuring secure, isolated, and reliable deployments for AI services in multi-stage pipelines.