Skill UI

This resource provides a minimal working example for implementing a complete digital document signing workflow using the Documenso SDK. It comprehensively guides users through creating a document, uploading a PDF file, adding designated recipients, placing specific signature fields, and finally triggering the signing process via code examples (TypeScript, Python) or REST API calls. Use this when integrating document signing into new applications or testing core document management capabilities.

A comprehensive set of production-ready design patterns for integrating with the Documenso SDK. It covers best practices for managing singleton clients, building typed service wrappers, robust API error handling (including status code analysis), and automatically implementing retry logic with exponential backoff. Ideal for establishing stable, scalable, and maintainable coding standards in TypeScript and Python.

A comprehensive guide for migrating from Documenso's legacy v1 API (document-centric) to the modern, recommended v2 API (envelope-based). This resource covers SDK upgrades for TypeScript and Python, providing code migration examples, best practices for implementing feature flags for gradual rollout, and steps for self-hosted container version upgrades.

Automates guidance for document merger tasks within business automation workflows, offering best practices, production-ready code, and validation to streamline merging, email, and spreadsheet operations.

This comprehensive workflow bundle guides the entire documentation lifecycle for software projects. It covers planning, API specification (OpenAPI), architectural design (C4 model), code commenting (JSDoc/TSDoc), generating READMEs, and maintaining structured knowledge bases (Wikis, Changelogs). Use this system when documenting a new product, maintaining an evolving codebase, or creating comprehensive technical guides.

This expert skill specializes in creating comprehensive, maintainable technical documentation directly from existing codebase, configurations, and comments. It can generate various artifacts, including API specifications, system architecture diagrams, detailed user guides, and technical references, ensuring consistency and accuracy using AI-powered analysis and industry best practices. Ideal for building robust documentation pipelines and standardizing documentation across large repositories.

This guide provides comprehensive templates and best practices for structuring all types of technical documentation. It covers essential formats including README files, REST API endpoints, code comments (JSDoc), Changelogs, Architecture Decision Records (ADR), and AI-optimized documentation. Use these templates to ensure your project documentation is standardized, scannable, and easily consumable by both human developers and AI agents.

Analyzes a codebase to surface subdomains (Core, Supporting, Generic) and recommend bounded contexts using DDD strategic design, helping teams clarify domain boundaries, cohesion, and service split decisions.

This skill specializes in building robust, scalable, and cloud-native backend microservices using the latest .NET 8 and C# 12 features. It masters modern architectural patterns, including Clean Architecture, CQRS with MediatR, and minimal APIs. Expertise covers secure authentication (JWT), data persistence with Entity Framework Core, asynchronous programming best practices, and comprehensive unit/integration testing, ensuring highly maintainable and performant code.

Provides deep expertise in analyzing DWARF v3-v5 debug files, verifying their integrity through llvm-dwarfdump, and helping parse or write code that interacts with DWARF data for more reliable debugging toolchains.

This skill provides comprehensive guidance on building reliable, fast, and maintainable end-to-end (E2E) test suites. It ensures code quality by validating critical user journeys and catching regressions in complex workflows before deployment. Essential for setting up robust CI/CD pipelines, cross-browser compatibility, and establishing rigorous QA standards.

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.