Skill UI

A comprehensive guide for optimizing Intercom API interactions. It covers advanced strategies such as implementing response caching (LRUCache), designing efficient search queries, handling large datasets with cursor-based pagination, managing connection pooling, and executing rate-aware parallel requests. Essential for developers building high-throughput, low-latency Intercom integrations.

This skill integrates LangChain 1.0 and LangGraph 1.0 into standard OpenTelemetry (OTEL) backends (e.g., Jaeger, Honeycomb, Tempo). It solves critical observability gaps by ensuring correct prompt content capture, propagating spans across complex subgraphs, and providing five LLM-specific Service Level Objectives (SLOs) like latency and cost. Ideal for multi-tenant, compliance-sensitive, or existing OTEL stack environments where LangSmith is unsuitable.

Provides comprehensive observability for Miro REST API v2 integrations. It captures critical metrics using Prometheus (request rates, latency, error tracking) and OpenTelemetry traces for end-to-end request flow visibility. Includes structured logging for detailed debugging and robust rate limit management, ensuring your applications are highly reliable and maintainable.

This guide provides advanced best practices for optimizing interactions with the Miro REST API v2. It covers essential patterns like cursor-based pagination to prevent over-fetching, implementing robust caching strategies (In-Memory/Redis) for reduced latency, and managing connections and concurrency for reliable bulk operations. It is designed for building scalable, high-throughput, and efficient integrations into Miro.

This skill provides a comprehensive framework for setting up robust observability for Navan API integrations in production environments. It guides users through implementing structured logging, tracking request latency, classifying various errors (auth, rate limit, server), and monitoring the OAuth token refresh lifecycle. Includes practical steps and YAML examples for configuring alerts and building dashboards in platforms like Datadog, CloudWatch, or Prometheus/Grafana.

A comprehensive wrapper for Notion API calls that implements full observability. It automatically tracks vital metrics including request rates, latency (average, p95), error counts, and rate limit status. This skill is essential for building reliable monitoring dashboards, setting up alerts, and ensuring the health of Notion integrations in production environments. It supports structured logging and Prometheus metrics.

This guide provides advanced strategies for optimizing interactions with the Notion API. Learn best practices such as implementing Time-To-Live (TTL) caching, batching multiple block updates, avoiding inefficient N+1 query patterns, and managing rate limits. Use this when your Notion application suffers from slow responses, high latency, or excessive API call consumption.

This guide provides comprehensive methods for building robust change detection and event handling within Notion workspaces. It compares the reliability, complexity, and latency of three approaches: polling using last_edited_time, utilizing native webhooks, and using third-party connectors. Learn how to implement reliable, incremental synchronization, change feeds, and event-driven workflows using the Notion API to ensure data consistency and near real-time updates.

This skill guides the setup of comprehensive observability for Palantir Foundry integrations. It covers structured logging, Prometheus metrics (latency, errors), health checks, and Prometheus alert rules, enabling robust monitoring of API integration health, performance, and reliability. Use this when implementing production-grade monitoring for Foundry APIs.

This guide helps determine the optimal deployment strategy for Qdrant, a vector database. It compares four main options—Local Mode, Self-Hosted (Docker), Qdrant Cloud (Zero-Ops), and Qdrant EDGE—based on project requirements such as control needs, operational overhead, data residency, and required latency. Use it to decide whether to prototype, manage full infrastructure, or prioritize minimal latency.

This guide provides detailed strategies for optimizing Qdrant's operational performance. It covers critical areas such as search speed (latency and throughput), efficient vector indexing strategies, and advanced memory management techniques. Use this when you need to improve the speed, scalability, and resource efficiency of your vector database deployment.

This guide provides comprehensive guidance on scaling Qdrant, a vector database. It helps users diagnose performance bottlenecks and determine the optimal horizontal or vertical scaling strategy. Strategies are categorized based on specific needs: increasing data volume, improving query throughput (QPS), minimizing query latency, or managing high query result volumes.