Skill UI

This guide provides comprehensive methods for load testing and capacity planning specifically for Salesforce integrations. It walks users through calculating API limit budgets, running high-volume performance tests using tools like k6, and optimizing Bulk API throughput. Use this when scaling complex, high-volume, or mission-critical Salesforce processes.

A comprehensive guide and implementation pattern for handling Salesforce API quotas and rate limits. Learn how to monitor daily usage, implement exponential backoff with jitter for transient errors (like REQUEST_LIMIT_EXCEEDED), and optimize API calls using bulk operations, caching, and queryMore to ensure stable, high-throughput integrations.

This guide provides methods for managing API rate limits, specifically addressing complex cost-based throttling models (like SalesLoft's). It teaches how to implement robust rate-limit-aware clients, handle 429 errors using exponential backoff, and calculate the total cost associated with deep pagination, ensuring stable and optimized API throughput for large-scale bulk operations.

A comprehensive skill for managing Shopify's unique rate limiting systems, covering both the REST API's leaky bucket mechanism and the GraphQL API's calculated query cost. It provides robust techniques for handling 429 errors, implementing exponential backoff, and optimizing queries to ensure stable, high-throughput e-commerce integrations.

A comprehensive guide and implementation demonstrating how to handle API rate limits when interacting with AI services like Together AI. It details the token bucket algorithm for client-side throttling and provides robust retry mechanisms (exponential backoff) to manage 429 (Rate Limit Exceeded) and 5xx server errors, ensuring stable, high-throughput batch processing.

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.

Provides strategic operational guidance for startup VPEs, focusing on maximizing delivery throughput using DORA metrics, optimizing the engineering hiring funnel, designing scalable team structures (squad/tribe), and establishing robust production disciplines. Use when scaling pains hit, delivery slows, or organizational design is unclear.

A throughput-first interrogation tool designed to pressure-test major engineering plans. It rigorously reviews six critical areas—including DORA metrics, cycle time, hiring funnel health, team structure, and production discipline—before committing to delivery milestones, hiring waves, or organizational changes.

This skill provides a comprehensive framework for auditing and improving the efficiency of GitHub Codespaces environments. It helps users identify waste sources like oversized devcontainers, inefficient machine types, and poorly scoped prebuilds. By following a structured workflow, users can reduce monthly cloud spending, drastically cut startup times, and maintain optimal development throughput.

Generates a complete, structured load and performance testing plan for any service. This skill guides the definition of test objectives, establishes measurable SLO thresholds (p99, error rate, throughput), and designs multi-scenario tests (Baseline, Stress, Spike, Soak). It is essential for teams needing to set clear pass/fail criteria, select appropriate tooling (k6, Locust), and integrate performance gates into CI/CD pipelines.

Use this skill for optimizing large-scale data ingestion, ETL, and data warehouse loading. It helps diagnose bottlenecks (such as extraction speed, network transfer, or loading time) and implements best practices like idempotent writes, native cloud scans, and checkpointing to achieve maximum processing speed while strictly guaranteeing data integrity and traceability.

A comprehensive guide for optimizing latency-critical systems, such as market data feeds, real-time dashboards, and streaming agents. It details structured methods for mapping 'hot paths,' tracking key metrics (p95, p99 latency, throughput, freshness), and implementing architectural improvements like caching and batching to ensure high performance and data freshness under load.