Skill UI

This skill guides users through setting up a robust, local development loop for Notion integrations. It covers creating dedicated development tokens, structuring the project for testability, implementing mocked SDK unit tests, and handling API rate limits with exponential backoff retries. This ensures rapid, reliable iteration and testing without compromising production data safety.

A guide to building production-grade, fault-tolerant integrations with Notion. Learn industry best practices for handling API failures, including exponential backoff retries, implementing circuit breakers to prevent cascade failures, utilizing offline caching for graceful degradation, and setting up health checks. Essential for maintaining high system uptime when interacting with the Notion API.

This skill provides a robust solution for managing API rate limits, particularly crucial when integrating with enterprise platforms like Palantir Foundry. It implements advanced techniques including exponential backoff, jittered retries for handling 429/5xx errors, and a token bucket rate limiter for controlled batch throughput. Use this to ensure reliable data ingestion and bulk API operations.

Master the art of building fault-tolerant e-commerce applications for Shopify. This guide covers essential reliability patterns, including implementing circuit breakers for API outages, ensuring webhook idempotency to handle retries, and utilizing queue systems and cached fallbacks for graceful degradation. Essential for building highly resilient integrations that withstand API failures and webhook storms.

This skill provides comprehensive strategies for handling rate limits when interacting with the Webflow Data API v2. It covers using SDK built-in retries, implementing custom exponential backoff with jitter, controlling concurrency using request queues, and optimizing calls with bulk endpoints (e.g., createItemsBulk). Use this guide when encountering 429 errors or when maximizing API throughput is critical for robust applications.

A critical discipline for managing expenses when integrating paid AI or inference APIs. This guard treats dollar cost as a third complexity dimension, mandating the definition of cost contracts (max calls per run, max budget per run, max budget per day). It enforces best practices for bounding loops, retries, and fan-out, ensuring that code bugs cannot lead to unexpected overspending. Best practice requires configuring hard caps both in the code and at the provider dashboard for defense in depth.

The Agent Harness provides a robust, verifiable loop structure for driving complex goals across specialized domains. It converts a high-level objective into a verifiable task plan, executes tasks sequentially using domain-specific tools, and rigorously verifies every step with machine-run checks. The system includes mechanisms for retries, budget management, and mandatory human escalation, ensuring no goal is closed until all progress is verifiably complete or explicitly waived.