Skill UI

This skill automates the configuration and setup of Guidewire applications across multiple environments (Development, Staging, Production). It manages configuration promotion, handles environment-specific OAuth credentials, and sets up separate integration gateway endpoints. It ensures strict data isolation, streamlining the entire CI/CD deployment process for enterprise insurance systems.

A comprehensive guide for configuring API access across development, staging, and production environments. This setup ensures strict isolation of API keys and environment-specific parameters (like models, speed, and caching). It covers best practices for secret management using major CI/CD and secret services (GitHub Actions, AWS Secrets Manager, GCP Secret Manager).

This skill provides comprehensive enterprise-grade access control (RBAC) for Instantly.ai workspaces. It enables administrators to manage workspace members, assign roles, govern API keys with granular scopes (e.g., read-only, specific campaign access), and set up workspace groups for agency operations. Essential for implementing secure, multi-user environments and maintaining strict data isolation.

A comprehensive guide and implementation structure for configuring Instantly.ai across Development, Staging, and Production environments. This skill ensures strict workspace isolation by utilizing environment variables and dedicated workspaces, preventing accidental data leakage or incorrect live deployments during testing and iteration cycles. It covers secure key management and safe promotion workflows.

This guide details the setup and management of the Juicebox AI platform across multiple isolated environments: Development, Staging, and Production. It ensures workspace isolation, controls data access, and maintains data integrity by differentiating sample datasets (Dev), full datasets with restricted exports (Staging), and full-featured production environments. Follow this workflow to safely deploy and test analyses from development to live production.

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.

This guide details best practices for configuring LangChain applications across development, staging, and production environments. It ensures strict environment isolation by managing separate API keys, model settings, and LangSmith project names. The setup utilizes Zod for robust validation and integrates secure secret management via GitHub Actions, AWS Secrets Manager, or GCP, guaranteeing reliable and secure deployments.

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 the best practices for configuring Langfuse across development, staging, and production environments. It covers essential steps including environment-specific API key management, secret integration (AWS/GCP), and implementing reliable CI/CD pipelines to ensure data isolation and prevent cross-environment leakage, maintaining observability throughout the software lifecycle.

This skill provides comprehensive best practices for managing data within AI agent workflows. It covers data classification (PII, PHI), prompt-level controls, secure memory usage, and isolation techniques. It ensures that agents adhere to global regulations like GDPR, CCPA, and HIPAA when handling sensitive or regulated data.

A comprehensive guide for configuring and managing multiple, isolated deployment environments (development, staging, production) for SaaS applications. It covers best practices such as using environment variables for configuration, implementing client factories for resource isolation, integrating secure secret management (e.g., GCP Secret Manager), and running validation scripts to ensure consistency and security across all deployment stages.

This comprehensive skill manages the entire lifecycle of database testing, ensuring data integrity and system reliability. It covers setting up isolated test environments (using Docker or in-memory databases), executing migrations, and implementing robust test isolation strategies (such as transaction rollback). Use cases include verifying CRUD operations, validating referential constraints, performance benchmarking (EXPLAIN ANALYZE), and ensuring backward compatibility across multiple databases (PostgreSQL, MySQL, MongoDB) and ORMs.