Skill UI

This solution implements robust Role-Based Access Control (RBAC) and multi-tenancy for third-party AI services. By utilizing an application proxy layer, it allows organizations to manage multiple teams, assign separate API keys, enforce granular content policies (e.g., filtering prohibited terms), and track daily budgets. This ensures secure, governed, and scalable resource usage in enterprise environments.

This skill provides a complete local development loop for Ideogram integrations. It includes a strongly typed client wrapper, a mock server for offline testing (preventing API credit usage), and comprehensive Vitest unit tests. Ideal for setting up robust, fast-iterating development environments and ensuring API reliability.

This skill guide provides advanced techniques for optimizing Ideogram API usage. It covers selecting optimal model tiers (e.g., Turbo vs. Standard) based on desired quality and speed, implementing prompt-based caching to reduce API calls and costs, and managing parallel generation with concurrency limits for high throughput. Additionally, it demonstrates CDN integration for rapid asset delivery.

This document provides production-grade patterns for integrating with the Ideogram API in TypeScript and Python. It addresses common challenges in API usage, including implementing singleton client management, robust exponential backoff retry logic for rate limiting, and strict response validation using libraries like Zod. Use this guide when building reliable, scalable applications that depend on external AI image generation services.

Defines Java 17+ coding standards for Spring Boot services, covering naming, immutability, Optional usage, streams, exceptions, generics, project layout, and testing practices to ensure readable, maintainable code.

This guide provides comprehensive metrics, instrumentation code (TypeScript), health check logic, and alerting rules for monitoring a SaaS platform like Juicebox. It covers critical metrics such as search latency (p95), data ingestion rates, API error rates, and quota usage, ensuring the maintenance of an optimal user experience and data integrity. Use this to establish robust operational monitoring dashboards and alerts.

This module provides a compliance-grade, tamper-evident logging solution for tracking all Kling AI operations. It records task submissions, status changes, and credential usage into structured, hashed logs. Use this when meeting regulatory compliance requirements or preparing for internal/external security audits.

This utility provides robust cost control mechanisms for integrating AI generation APIs. It implements daily budget enforcement, per-request cost estimation, and usage alerting. Developers can use it to prevent unexpected overspending and effectively manage credits and resources when building AI-powered applications.

This skill provides comprehensive management for team access to AI APIs, ensuring security and accountability. It allows setting up role-based access control (admin, editor, viewer), implementing per-member and total team usage quotas, and managing separate API keys across Development, Staging, and Production environments. Ideal for scaling AI usage in enterprise settings.

A comprehensive guide to optimizing LLM API usage within LangChain frameworks. Techniques include implementing token usage tracking, model tiering to route tasks based on complexity, utilizing caching to eliminate duplicate calls, prompt compression for efficiency, and enforcing strict budget limits to manage operational costs effectively.

A comprehensive utility designed to gather all necessary diagnostic evidence for troubleshooting complex LangChain failures. It captures environment snapshots, dependency versions, detailed execution traces (including LLM calls and tool usage), and error stack traces. Use this bundle when preparing detailed GitHub bug reports or conducting LangSmith tracing.

This module implements comprehensive Role-Based Access Control (RBAC) for multi-tenant LangChain applications. It provides granular permission models for controlling model access, tool execution, and chain invocation. Key features include multi-tenant data isolation in vector stores, enforcing usage quotas (token limits), and integrating middleware for robust security enforcement. Ideal for building secure, scalable, and compliant enterprise LLM systems.