Skill UI

Guide for assessing AWS, Azure, and GCP environments with tools such as Pacu, ScoutSuite, and Prowler to uncover IAM weaknesses, exposed storage, insecure serverless functions, and cloud-native attack chains.

Detect unauthorized crypto-mining in AWS, Azure, and GCP by correlating GuardDuty/Defender/SCC findings, compute anomalies, network flows, and container/serverless behaviors to validate threats and guide response.

Provides a workflow for authorized penetration testing across AWS, Azure, and GCP to discover IAM, storage, serverless, and compute weaknesses, leveraging Pacu, ScoutSuite, Prowler, and cloud-native tools.

This sophisticated tool automates the security review of serverless functions across major cloud providers, including AWS Lambda, Azure Functions, and GCP Cloud Functions. It systematically identifies critical misconfigurations such as overly permissive IAM execution roles (wildcard actions), hardcoded secrets in environment variables, and potential lack of runtime protections. Ideal for pre-production auditing and compliance checks.

This comprehensive tool manages the complete lifecycle of ML experiments, providing robust deployment capabilities across diverse environments. It supports running training jobs on local GPUs, dedicated remote servers, Vast.ai, and serverless platforms like Modal. The workflow handles environment detection, code synchronization (rsync/git), dependency pinning, and integrates advanced logging tools (e.g., W&B), ensuring reliable, scalable, and reproducible ML training runs.

This guide outlines a comprehensive methodology for hardening AWS Lambda execution roles. It focuses on implementing the principle of least privilege by auditing existing roles, analyzing actual API usage via CloudTrail and IAM Access Analyzer, and programmatically creating scoped IAM policies. Use this when deploying new serverless functions, remediating over-privileged roles found during security audits, or building secure, reusable role templates.

A comprehensive guide to securing serverless compute platforms (AWS Lambda, Azure Functions, Google Cloud Functions). It covers critical security aspects including enforcing least-privilege IAM roles, integrating secrets management, vulnerability scanning of dependencies, input validation, and implementing runtime monitoring for robust DevSecOps practices.

A comprehensive guide to designing scalable, cost-effective, and compliant architectures on Microsoft Azure. This skill covers the entire lifecycle, from gathering detailed business requirements (SLA, RPO/RTO) and selecting appropriate service patterns (AKS, Serverless, Data Factory), to generating Infrastructure as Code (IaC) using Bicep. It also includes steps for cost optimization, reserved instance planning, and setting up robust CI/CD pipelines via Azure DevOps or GitHub Actions.

This comprehensive workflow guides the design, optimization, and deployment of scalable, cost-effective Google Cloud Platform (GCP) architectures for both startups and large enterprises. It covers the full lifecycle, from gathering detailed requirements (compliance, SLA) and recommending optimal patterns (Serverless, GKE, Data Pipelines) to estimating costs, generating Infrastructure as Code (IaC) using Terraform, setting up CI/CD pipelines, and completing a thorough security review.

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.

Utilize Modal, a serverless GPU cloud, for running complex machine learning workloads, including training, fine-tuning, and inference. This tool provides a zero-config environment, eliminating the need for SSH setup or Docker management. It features automatic scale-to-zero billing and local-first execution, making high-powered GPU compute accessible for debugging and small-scale experiments.

Modal is a serverless cloud platform for running Python code on demand, including GPU-accelerated AI/ML workloads. Use for deploying models, serving web APIs, batch processing, and scaling Python applications to cloud containers with automatic scaling from zero to thousands of instances. Supports custom container images, persistent storage, and sub-second cold starts for low-latency inference.