Skill UI

Designs comprehensive, production-ready observability strategies by integrating metrics, logs, and traces. This tool guides the setup of SLI/SLO frameworks, optimizes noisy alerting configurations, and generates detailed dashboards, ensuring deep visibility into service health and performance.

This skill specializes in implementing Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to enforce reliability standards. It guides users in designing comprehensive SLO frameworks, calculating error budgets, and building robust monitoring systems. Use this when you need to establish data-driven reliability targets, balance feature velocity with technical health, and align service performance directly with core business objectives.

A comprehensive guide and set of scripts to systematically audit AWS S3 buckets for security misconfigurations. This tool identifies publicly exposed data, overly permissive ACLs, inadequate bucket policies, and missing encryption settings, ensuring adherence to least-privilege principles and compliance frameworks like SOC 2 or HIPAA.

This system provides a structured workflow for tracking security vulnerability exceptions and formally accepting related risks. It mandates documentation of compensating controls, manages required approval chains, and automatically handles expiration based on defined Service Level Agreements (SLAs). Essential for maintaining compliance and visibility into accepted risks, supporting frameworks like PCI DSS, SOC 2, and NIST CSF.

A proactive, hypothesis-driven skill designed to hunt for Advanced Persistent Threats (APT) within complex enterprise environments. It analyzes endpoint telemetry, network logs, and memory artifacts using frameworks like MITRE ATT&CK. Use this skill when investigating anomalous behavior flagged by UEBA, conducting scheduled threat sprints, or validating exposure to known TTPs using tools like Velociraptor and osquery.

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.