Skill UI

This comprehensive checklist provides guidance for integrating the Wispr Flow voice-to-text API into a production environment. It covers best practices for both WebSocket and REST API usage, addressing critical elements like authentication, robust error handling (e.g., 401 Unauthorized or network disconnections), and optimizing performance. It is essential for building reliable, voice-powered applications, especially those handling complex contexts like programming code and CLI commands.

This comprehensive checklist provides guidance for integrating Workhuman Social Recognition and its rewards API into production environments. It covers essential API concepts, including OAuth 2.0 authentication, peer-to-peer recognition workflows, configurable award levels, and bidirectional synchronization with major HRIS systems (e.g., Workday, SAP). Use this resource when building robust and scalable employee recognition or reward management systems.

This skill guides the implementation of robust GraphQL APIs using Golang. It covers best practices for schema design (SDL), writing efficient resolvers, and critically, preventing N+1 database query issues using DataLoaders. Essential for building scalable, production-ready backend services.

This skill guides the end-to-end development, implementation, and validation of production-ready Python modules for Azure IoT Edge. It covers critical aspects including telemetry processing, protocol adaptation (Modbus, OPC-UA), local inference, and ensuring robust, secure edge-to-cloud messaging under variable network conditions.

Squirrel is an advanced, full-cycle AI coding skill designed to guide software development from concept to production. It automatically detects the current project state (greenfield, in-progress, or mature) and adapts an 8-phase engineering pipeline. This comprehensive workflow covers planning, building, rigorous testing, bug hunting, code polishing, documentation, and deployment readiness, ensuring high-quality, maintainable results.

Performs a comprehensive external audit of a deployed repository's state, going beyond simple code review. It checks for critical production gaps, including RLS misconfigurations, webhook idempotency issues, secret exposure, deployment health, and prompt injection vulnerabilities. Ideal for pre-launch gates and ensuring code stability before going live.

This skill guides the design and optimization of production-grade multi-agent systems. It specializes in using LangGraph, LangChain, and DeepAgents to create structured, complex AI workflows. It covers state management, agent routing, memory integration, and tool calling, making it ideal for building robust, scalable agents like supervisors, planners, and coders.

This skill guides the safe execution of chaos engineering experiments to surface weaknesses in production systems without causing actual outages. It provides tools to design robust experiments by defining hypotheses, calculating the potential 'blast radius' before failure injection, setting strict abort criteria, and generating structured postmortems. Ideal for SRE teams running game days and resilience tests.

A structured methodology for validating non-trivial decisions by subjecting them to an adversarial, fresh-context review. This process is crucial when correctness is paramount—such as in production, security-sensitive logic, or complex architecture—by forcing a skeptical examination that aims to disprove assumptions rather than simply approve them.

A comprehensive pre-production audit tool that scans a codebase for critical issues across eight dimensions: security, database, deployment, code quality, AI/LLM usage, dependencies, frontend, and observability. It intercepts deployment commands, forcing users to resolve critical failures before shipping code. The audit is stack-agnostic and provides detailed pass/fail/manual reports.

This guide assists developers in building robust Model Context Protocol (MCP) servers and clients using C#/.NET. It specifically targets the stable 1.x NuGet packages, providing detailed instructions for configuring various transports (STDIO, Streamable HTTP), defining custom tools, prompts, and resources. It helps solve common integration issues like stale preview pinning, complex HTTP wiring, and structured content handling, ensuring production-ready, standard-compliant code.

This guide outlines architectural patterns and best practices for building scalable, production-ready APIs with FastAPI. It covers proper project structure, separating concerns using routers, services, and CRUD layers, implementing dependency injection for database sessions and authentication, defining Pydantic schemas for request/response, handling asynchronous operations, and managing custom error responses.