Skill UI

This guide teaches security teams how to proactively detect and respond to unauthorized cryptocurrency mining operations across major cloud platforms (AWS, Azure). It utilizes a multi-layered approach by analyzing compute cost anomalies, monitoring GuardDuty findings, inspecting network traffic to known mining pools, and analyzing runtime process execution in EC2, ECS, and EKS environments.

A comprehensive guide to deploying cloud-native deception across AWS, Azure, and GCP. This skill utilizes decoy resources—such as canary IAM keys, honey object storage buckets, and decoy secrets—whose sole purpose is to generate high-fidelity alerts the instant they are interacted with by an attacker. It detects cloud reconnaissance, credential theft, and lateral movement by wiring detection through native logging services (CloudTrail, Sentinel, Cloud Audit Logs), ensuring near-zero false positives for the SOC.

This tool automates the OAuth 2.0 scope minimization review process. It identifies over-permissioned third-party application integrations, excessive API scopes, and risky OAuth consent patterns across identity providers. It is crucial for security audits, compliance (GDPR, SOC 2), and enforcing the least-privilege principle for cross-boundary data access.

This skill provides a structured approach to monitoring and analyzing data leak sites (DLS) maintained by ransomware groups. By extracting structured data on victim postings, threat group tactics, and sector-specific details, users can assess current ransomware risk, track attack trends, and proactively build robust defense strategies.

This skill automates the entire phishing incident response workflow using the Splunk SOAR REST API. It parses suspicious email headers and bodies to extract artifacts (URLs, IPs, sender details), creates a structured incident container, triggers a comprehensive automated investigation playbook, and compiles a final verdict report for security teams.

This skill guides the deployment of decoy files, or "canary tokens," across critical file systems. These files act as tripwires, monitoring for unauthorized changes (modification, deletion, renaming). By tracking these high-value decoys, systems can detect ransomware encryption activity in real-time, providing a low false-positive, high-fidelity detection layer that complements traditional EDR and backup solutions.

Implements HashiCorp Vault's dynamic secrets engine to automatically generate, lease, and rotate short-lived credentials for databases (e.g., Postgres, MySQL), AWS IAM keys, and PKI certificates. This eliminates the risk associated with storing static secrets, ensuring compliance with zero-trust principles and automating the entire secret lifecycle.

A comprehensive framework for managing third-party risk (TPRM) throughout the entire vendor lifecycle. This tool guides users through inventory, risk tiering, running due diligence questionnaires (SIG, CAIQ), reviewing security evidence (SOC 2, ISO 27001), codifying requirements in contracts, and establishing continuous monitoring protocols. It is essential for maintaining compliance, managing supply chain risk, and securing data handled by external vendors.

This skill details how to monitor Certificate Transparency (CT) logs using industry tools like crt.sh and Certstream. By analyzing publicly issued SSL/TLS certificates, users can proactively detect lookalike domains, unauthorized certificate issuance for owned assets, and early-stage phishing infrastructure targeting their organization. Ideal for SOC analysts and threat hunters.

A comprehensive guide detailing the systematic workflow for analyzing malicious Linux ELF binaries. It covers essential techniques including static analysis (using readelf, elftools), dynamic tracing (strace, ltrace), and deep reverse engineering (GDB) of various threats like botnets, cryptominers, and ransomware targeting Linux environments, containers, and cloud infrastructure.

A comprehensive, adaptive framework for developing and implementing features, adhering to a strict, multi-phase workflow: Specify, Design, Tasks, and Execute. This skill ensures feature quality through granular task breakdown, atomic Git commits, requirement traceability, and mandatory, independent verification. It automatically adjusts its depth based on feature complexity (Small to Complex), providing a robust mechanism for managing technical debt and capturing project lessons.

This skill manages the entire lifecycle of long-running deep learning training and inference jobs deployed on remote, ephemeral computing environments (e.g., AutoDL, RunPod, bare SSH, Slurm, K8s). It provides critical capabilities like resilient checkpointing, managing spot instance preemption, debugging resource failures (OOM, NaN), and ensuring safe billing teardown, making it essential for professional ML Ops on rented resources.