Skill UI

A comprehensive toolkit designed to monitor the health and performance of Guidewire InsuranceSuite. It tracks critical operational metrics including claim processing times, policy synchronization rates, billing accuracy, API latency, and batch job statuses. The system provides real-time alerting, structured logging, and health checks to maintain high service level agreements (SLAs) for complex insurance workflows.

Automates Harvest time logging, project/client/task management, and invoicing insights via Claude Code natural language commands from your terminal, speeding up billing and reporting workflows.

A comprehensive skill designed to manage, track, and persist image assets generated by Ideogram's API. This tool addresses the critical issue of expiring image URLs by implementing a complete lifecycle pipeline. It handles metadata tracking (prompt, seed, model, style, etc.), immediate downloading, and robust storage to both local filesystems and cloud solutions like S3. Features include asset persistence, generation history logging, and image reproduction using stored seed values.

Automates choosing and configuring database audit logging strategies, offering trigger/application/CDC/log-based options, schema templates, and guidance for compliance or debugging workflows.

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.

A comprehensive utility for structured logging, request tracing, and diagnostic purposes when integrating with the Kling AI API. It automatically captures request/response pairs, task lifecycle events, and detailed timing metrics for every API call, making troubleshooting video generation pipelines efficient and systematic. Ideal for developers building robust, observable AI applications.

This guide outlines essential security practices for deploying production-grade LLM applications using LangChain. It covers secure secrets management, defending against prompt injection, implementing sandboxed tool execution, validating model outputs for sensitive data, and setting up comprehensive audit logging, ensuring robust and secure AI deployment.

This comprehensive skill implements advanced event-driven patterns for LangChain applications. It provides custom callback handlers to observe every stage of chain execution (e.g., tool start/end, LLM token generation). Furthermore, it supports real-time data transmission via Server-Sent Events (SSE) and WebSockets, and integrates webhooks for centralized event logging and comprehensive tracing.

This guide details setting up enterprise-grade access control for Langfuse. Learn how to configure role-based permissions (Owner, Admin, Member), implement scoped API keys, integrate SSO (SAML/OIDC), and set up comprehensive audit logging. It is essential for managing multi-team isolation and ensuring compliance in production environments.

This comprehensive guide details how to configure robust Role-Based Access Control (RBAC) for Lindy AI workspaces. It is essential for setting up team permissions, managing complex workspace access, and enforcing enterprise security policies. Learn how to implement advanced features like SSO, SCIM synchronization, and detailed audit logging to ensure data security and separation of duties across large organizations.

A comprehensive, production-ready toolkit for robustly debugging and monitoring Linear API integrations. This bundle includes features like an instrumented client wrapper for detailed request/response logging, a performance request tracer with summary statistics (avg, p95, max latency), a health check utility, and an environment validator. Ideal for developers setting up logging, performance monitoring, or debugging utilities in a live SaaS environment.

A comprehensive guide and implementation using TypeScript to establish production-grade observability for Linear API integrations. This includes defining Prometheus metrics (tracking latency, error rates, rate limits, and webhooks), implementing structured logging (using pino), and setting up robust health check endpoints. It ensures full visibility into API performance and system health, critical for mission-critical SaaS applications.