Skill UI

This framework implements robust guardrails for LLM applications, validating both user inputs and model outputs. It detects prompt injection, sensitive PII (Personally Identifiable Information), toxic content, and ensures structured output compliance using advanced tools like NVIDIA NeMo Guardrails and Guardrails AI. Essential for building secure, reliable chatbots and RAG systems.

This comprehensive tool generates high-quality Mermaid diagram code (including flowcharts, sequence diagrams, ER diagrams, etc.) directly from natural language user requirements. It handles complex structured output, mandatory syntax validation using dedicated CLI tools, and detailed visual review to ensure the accuracy of all components and connections.

A comprehensive guide detailing the workflow for firmware reverse engineering. It uses tools like binwalk to identify embedded filesystems (e.g., SquashFS, CramFS), perform entropy analysis to locate hidden data, and extract components. The process covers deep inspection, credential discovery, and reporting for IoT and embedded security assessments.

This advanced assistant profiles specified targets (scripts, processes, GPU, memory, etc.) to identify performance bottlenecks. It selects appropriate profiling strategies, runs external tools (e.g., cProfile, nvidia-smi), and can write code instrumentation to measure CPU, GPU, memory, and interconnect overheads. It generates a detailed, actionable report.

This utility ensures the prevention of silent floating-point mismatch bugs across all EVM chains. It covers runtime querying of decimals, chain-aware caching, handling precision drift in bridged tokens, and safe normalization for bots, dashboards, and DeFi tools. It is crucial for accurate cross-chain balance comparison.

A governance skill designed to secure AI agent tool calls using advanced authorization policies. It implements Cedar policies to control which tools agents can access and under what conditions. Key features include running in 'Shadow mode' for safe observation, 'Enforce mode' for strict blocking, and generating cryptographically signed Ed25519 receipts to provide an immutable, tamper-evident audit trail for every policy decision.

A comprehensive guide for developing robust, Unix-native Command Line Interfaces (CLIs) using Go. It details best practices for command structure, flag handling, configuration layering (Viper), and argument validation using Cobra. Learn how to build, extend, and review professional-grade Go tools that behave predictably in shell environments.

A comprehensive guide and toolset for Go code quality assurance using `golangci-lint`. It enables developers to run multiple linters in parallel, configure best practices via `.golangci.yml`, automate fixes, and interpret complex lint output. Ideal for maintaining code standards, setting up CI/CD pipelines, and cleaning up legacy codebases.

Improves Go error handling by treating errors as structured data. This utility adds critical context—such as domain, user IDs, stack traces, and machine-readable error codes—to standard Go errors. This capability is vital for enhancing observability, enabling APM tools (like Sentry or Datadog) and on-call engineers to diagnose complex issues quickly without relying on manual log correlation.

This skill provides robust mechanisms to verify the integrity of AI agent plugins, tools, and dependencies. It generates deterministic SHA-256 manifests (INTEGRITY.json) for all components, allowing users to detect unauthorized modifications, track provenance, and ensure that deployed assets match their published versions. Use this during CI/CD pipelines, code review, and pre-production promotion to secure the entire agent ecosystem.

This toolset provides comprehensive automation for Cal.com scheduling operations through Rube MCP. It enables users to manage the entire booking lifecycle, including listing and creating bookings, checking real-time availability and finding open slots, configuring and managing webhooks for event triggers, and managing organizational teams. It is essential for integrating scheduling workflows into larger automation processes.

This skill guides the generation of a complete, production-ready Model Context Protocol (MCP) server using C# and .NET. It covers advanced architectural patterns, including Host builder setup, proper dependency injection, structured logging, and implementing robust functional tools. Ideal for developers building sophisticated AI agents or backend services that require standardized communication and tool invocation capabilities.