Skill UI

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.

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.

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 guide details the best practices for configuring and isolating AI agents across development, staging, and production environments. It covers the setup of separate workspaces, secure secret management using tools like Vault/AWS/GCP, and integrating environment-aware logic into CI/CD pipelines to ensure data integrity and smooth promotion from dev to prod.

This skill provides a comprehensive command-line interface (CLI) for managing the entire lifecycle of projects, issues, and teams within the Linear product management platform. Users can create, update, list, and change the status of issues and entire projects, ensuring proper traceability and organized development cycles. It supports direct integration using Linear CLI commands for robust workflow automation.

This comprehensive checklist provides best practices and patterns for deploying Linear integrations to a production environment. It covers critical areas including robust authentication and secure secret management, error handling and system resilience (rate limiting, timeouts), performance optimization (caching, batching), and setting up full observability (health checks, structured logging). Use this when preparing for a go-live or auditing existing deployments.

Production-ready shell scripts covering Linux backups, monitoring, user management, log analysis, and automation for security and pentest environments.

This guide teaches comprehensive security hardening for API integrations. Learn best practices for credential management (using environment variables/secret managers), implementing git hooks to prevent secret leaks, robust input validation (using Zod), structured audit logging, and mandatory key rotation procedures, ensuring system compliance and operational security.

A comprehensive guide detailing essential security measures for integrating Mistral AI models. Covers API key management, defending against prompt injection, implementing content moderation, sanitizing outputs, and secure logging practices, ensuring robust and safe application development.

A comprehensive technical audit checklist designed to validate the production readiness of OpenRouter API integrations. It covers critical areas including security practices (e.g., secret management, key rotation), system reliability (e.g., fallbacks, retries, timeouts), and observability requirements (e.g., structured logging, cost tracking). Use this guide before launching or during major operational reviews to ensure robust, scalable, and compliant deployments.

High-level PyTorch Lightning streamlines training loops with automatic distributed support (DDP/FSDP/DeepSpeed), callbacks, checkpointing, logging, and device management so you can scale from a laptop to multi-node clusters without boilerplate.