Skill UI

Provides a detailed, multi-platform workflow to monitor long-running computational experiments. It checks the status of jobs across various environments (SSH, vast.ai, Modal), collects screen logs, fetches structured JSON results, and pulls detailed training metrics (loss curves, accuracy) from Weights & Biases (W&B). Essential for MLOps and automated result review.

This comprehensive guide provides best practices for hardening container images, dramatically minimizing the attack surface in production environments. It covers advanced techniques such as implementing multi-stage builds, using distroless images, running containers as non-root users, and configuring read-only filesystems. Adherence to CIS Docker Benchmark and secure DevSecOps practices are key focuses.

A comprehensive guide for conducting deep disk forensics investigations. This method covers secure evidence acquisition, creating bit-for-bit forensic images, analyzing file system structures (MFT, inodes), recovering deleted files, and reconstructing timelines using industry-standard tools like Autopsy, FTK Imager, and The Sleuth Kit. Essential for legal, HR, and security incident response.

Develops comprehensive profiles for Advanced Persistent Threat (APT) groups, criminal organizations, and hacktivist collectivities. This tool aggregates TTP documentation, campaign data, and attribution indicators from major intelligence sources (MITRE ATT&CK, Mandiant, CrowdStrike) to help organizations update threat models, prioritize defensive controls, and prepare executive briefings on sector-specific risks.

Kubesec is an open-source security risk analysis tool that scores Kubernetes resource manifests to identify security misconfigurations, privilege escalation risks, and policy violations. It checks for best practices like immutable root filesystems and non-root execution. Ideal for integrating into CI/CD pipelines, pre-deployment manifest review, or performing security audits.

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 professional-grade 'Living Pipeline' that integrates advanced AI reasoning into the SDLC. It provides autonomous guardrails for system security, cloud cost management (FinOps), and Kubernetes compliance. It audits Linux Kernel patches for memory vulnerabilities, detects cost drifts in Terraform plans, and generates hardened K8s manifests from natural language policies.

Master the use of the `gh` CLI to interact with core GitHub features. This guide covers managing issues, checking Pull Request statuses, listing and debugging CI workflow runs, and executing advanced data queries via the GitHub API. Essential for automating DevOps workflows and debugging CI/CD pipelines directly from the terminal.

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.

Manages production-grade secret infrastructure, covering Vault clusters, cloud secret stores, auth methods, rotation, and audit patterns to harden DevOps pipelines and respond to leaks.

This utility executes atomic Red Team tests mapped to the MITRE ATT&CK framework. It is essential for purple team exercises, allowing security teams to assess detection coverage gaps, validate SIEM/EDR rules, and generate detailed ATT&CK coverage heatmaps. Use it to build continuous detection validation loops and measure blue team visibility against known adversary techniques.

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.