Skill UI

A comprehensive utility suite for handling external API rate limits, specifically designed for services like TwinMind. It implements advanced patterns including exponential backoff with jitter and request queue management. Use this to ensure resilient, high-throughput, and stable interactions with resource-constrained AI APIs, minimizing 429 errors.

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

This guide details how to implement environment isolation (dev, staging, prod) for GPU cloud deployments on Vast.ai. It uses environment variables and configuration classes to enforce separate API keys, budget caps, instance limits, and GPU whitelists for each deployment tier, ensuring efficient resource management and cost control.

This skill provides robust strategies and Python implementations for dealing with API rate limiting (HTTP 429 errors) when interacting with services like Vast.ai. It includes automatic retry logic, exponential backoff algorithms, and optimized techniques for batch operations and polling, ensuring stable and efficient API throughput.

This guide provides comprehensive solutions for handling Vercel API rate limits (HTTP 429 errors). It covers implementing robust retry logic with exponential backoff for consuming Vercel's outbound APIs. Furthermore, it demonstrates how to configure Vercel's WAF rate limiting and custom edge configurations to protect your own deployed API endpoints, ensuring stability and preventing service abuse.

This comprehensive guide details the credit-based usage model of the Windsurf AI platform. It explains how various powerful models (e.g., SWE-1, GPT-4o) consume credits and provides strategies for optimizing AI usage costs. Learn how to monitor remaining credits, understand different pricing tiers (Free, Pro, Teams), and implement prompt engineering techniques to maximize efficiency and prevent mid-session interruptions.

Implements comprehensive security controls for the entire API key lifecycle, including secure generation, hashing, storage, rotation, and revocation. This skill involves designing keys with sufficient entropy, enforcing per-key scoping, setting rate limits, and building automated workflows to prevent leakage and unauthorized access. Essential for robust server-to-server authentication.

This skill covers implementing robust API rate limiting and throttling controls using advanced algorithms like token bucket and sliding window. It teaches how to protect backend systems against attacks such as brute force, credential stuffing, and resource exhaustion. Key components include configuring limits per user, per IP, and per endpoint, utilizing Redis for distributed counting, and implementing proper HTTP 429 responses for effective abuse prevention.

This tool automates the entire process of compiling a LaTeX source file into a submission-ready PDF. It handles multi-pass compilation, diagnoses common LaTeX errors (e.g., missing packages, undefined references, bibtex syntax errors), and iteratively attempts fixes to ensure the final document is accurate and adheres to specified page limits.

This tool provides a conservative, resource-aware snapshot of the current process's environment, including CPU, memory, disk, and accelerator limits. It helps users plan workloads accurately by providing clear boundary constraints (cgroup, scheduler, host limits) without performing stressful tests or making assumptions about usability.

This guide provides comprehensive troubleshooting for common issues encountered when converting Figma designs to code using the Anima SDK. It covers authentication failures (Figma/Anima tokens), specific node/file lookup errors, code rate limits, and output quality problems, helping developers ensure accurate and robust code generation.

This guide details advanced strategies for optimizing Claude API usage to reduce latency and cost. Techniques covered include prompt caching (significant cost reduction), strategic model selection (Haiku, Sonnet, Opus) based on task complexity, implementing streaming for perceived speed, and managing token limits. Use this guide when building high-performance, cost-efficient LLM applications that interact with large language models.