Skill UI

This tool analyzes historical sprint velocity data to generate a comprehensive engineering team health report. It moves beyond simple charts by diagnosing underlying delivery patterns, identifying risks like chronic over-commitment, and assessing story point calibration. The report provides actionable recommendations for improving capacity planning and team predictability.

A comprehensive, structured guide for designing large-scale distributed systems. Use this framework to structure interview answers or real-world architectural proposals, covering everything from clarifying requirements and estimating capacity to deep component design, data flow, and critical trade-offs.

A comprehensive framework for assessing the operational and cultural health of a team. It covers key dimensions such as delivering value, morale, psychological safety, and release capability. Use this guide for structured retrospectives, leadership reviews, or post-incident analyses to identify root causes and generate actionable improvement plans.

A comprehensive guide and shared vocabulary for designing and structuring complex software modules. Use this when you need to refine an existing module's interface, identify opportunities for deepening its capabilities, determine optimal internal seams, or improve code testability and AI-navigability. It establishes clear principles for Module, Interface, Depth, and Seam.

This meta-skill provides a comprehensive framework for the entire lifecycle of AI agent skills. It supports creating, refactoring, rigorously evaluating (e.g., Skill OS 2.0 Gates), and securely packaging skills from various sources like workflows, prompts, and documents. It ensures standardized, team-ready, and governed deployment of advanced AI capabilities.

This skill guides the architecture and implementation of SAML 2.0 identity federation between on-premises Active Directory and Microsoft Entra ID (Azure AD). It covers establishing seamless Single Sign-On (SSO) capabilities, eliminating the need for separate credentials and ensuring secure, cross-domain authentication for cloud applications.

This framework provides comprehensive detection capabilities for compromised cloud credentials across major platforms including AWS, Azure, and GCP. It analyzes critical threat indicators such as anomalous API activity, impossible travel patterns (impossible login locations), and unauthorized resource provisioning. Designed for incident response, security monitoring, and building robust detection rules to mitigate account takeover and credential abuse.

This skill provides comprehensive functionality for monitoring and mapping adversary-controlled assets, such as C2 servers, phishing domains, and exploit kit hosts. By leveraging tools like Passive DNS, Certificate Transparency logs, and specialized scanners (Shodan/Censys), analysts can discover, track, and pivot through complex threat actor infrastructure over time, significantly enhancing security operations and threat intelligence maturity.

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.