Skill UI

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

This deep dive guides users through migrating complex GPU workloads, such as deep learning training pipelines, from major hyperscalers (AWS, GCP, Azure) or other cloud platforms to Vast.ai. It covers critical steps including cost comparison analysis, adapting Docker images for new environments, securely handling cloud credentials via environment variables, and implementing robust validation and rollback procedures to ensure a smooth transition.

This skill provides a comprehensive framework for performing end-to-end UI testing and verification on Android emulators. It guides users through systematic processes including device calibration, UI element discovery using uiautomator, performing complex interactions (tap, swipe, input), and robust reporting via visual screenshots and logcat analysis. Ideal for ensuring the stability and functional correctness of React Native or native Android applications.

A comprehensive guide detailing techniques for detecting and exploiting Blind Server-Side Request Forgery (SSRF). It uses advanced out-of-band (OOB) techniques, such as Burp Collaborator and timing analysis, to interact with internal services, scan private IP ranges, and extract cloud metadata (AWS, GCP, Azure) even when the application does not reflect the server response directly. Essential for web application penetration testing.

This skill focuses on detecting and mitigating sophisticated code injection and privilege escalation attacks across the entire serverless ecosystem (AWS Lambda, Azure, GCP). It combines static analysis of dangerous code sinks (e.g., eval, exec) with continuous monitoring of event sources (S3, SQS, API Gateway) and rigorous IAM auditing. It provides a comprehensive framework to secure the expanding serverless attack surface.

A comprehensive guide for setting up production-grade CI/CD pipelines for Go projects using GitHub Actions. This skill covers essential quality gates including unit testing, linting (golangci-lint), static analysis security testing (SAST), vulnerability scanning, dependency management, Docker builds, and automated releases using GoReleaser.

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.

This harness provides comprehensive, reproducible evaluation pipelines for complex LLM chains and agents (LangChain/LangGraph 1.0). It integrates golden dataset management, LangSmith evaluation, RAGAS metrics, deepeval LLM-as-judge, and structured agent trajectory analysis. Use it when establishing quality benchmarks for new chains, diagnosing performance regressions after model switches, or implementing CI/CD gates to prevent quality drops.

A comprehensive guide for executing and reporting robust statistical analyses for manuscripts in Global Change Biology (GCB). This skill ensures the analysis meets the high standards of reproducibility required for publicly archived data (DOI). It covers advanced methods including mixed/hierarchical modeling, time-series and spatial analysis, meta-analysis, and rigorous uncertainty quantification (parameter, structural, and scenario). It emphasizes statistical rigor over result fabrication, guiding the use of appropriate inferential machinery based on data structure.

This guide provides a comprehensive framework for conducting rigorous cross-national data analysis, essential for academic research in international business. It covers critical advanced statistical methods like Measurement Invariance Testing (MGCFA), Multilevel Modeling (HLM), and Multi-Group SEM. It instructs users on handling cross-level moderation, interpreting country-level effects, and ensuring results are statistically auditable across complex data structures (e.g., panels, dyads).