Skill UI

This skill guides the process of transforming complex, raw data into compelling, actionable narratives. It covers the core structure (Setup, Conflict, Resolution), narrative arcs, and the three pillars (Data, Narrative, Visuals) needed to create high-impact presentations for executives, investors, and non-technical stakeholders. It ensures insights drive clear decisions.

A comprehensive runbook designed for on-call engineers to manage Databricks incidents, outages, and major job failures. It provides structured procedures for immediate triage, decision tree guidance (covering cluster failures, code errors, and data quality issues), and steps for evidence collection and postmortem documentation, ensuring rapid and systematic incident resolution.

Guides engineers through DeepSpeed distributed training and NVMe I/O optimizations, covering ZeRO, pipeline parallelism, mixed precision, and DeepNVMe handles for synchronous and asynchronous tensor storage.

This skill teaches the critical principles and decision-making processes for safe production releases. It covers platform selection (e.g., Kubernetes, Vercel), mandatory pre-deployment verification (Code Quality, Environment), structured workflows (Prepare, Backup, Deploy, Verify), and advanced strategies like Blue-Green and Canary deployments. Focuses on thinking through risk mitigation, not just executing scripts.

Guides engineers through selecting software architecture patterns, structuring projects, and documenting trade-offs via checklists, decision frameworks, and validation steps for system design conversations or new projects.

Guides Claude Code skill authors through designing and structuring workflow-based skills with numbered phases, decision routing, subagent delegation, and progressive disclosure so multi-step automations remain predictable and auditable.

Guides users through a three-stage workflow for collaborative documentation, helping teams gather context, iteratively refine structure, and test readability before sharing specs, proposals, or decision docs.

This guide provides comprehensive templates and best practices for structuring all types of technical documentation. It covers essential formats including README files, REST API endpoints, code comments (JSDoc), Changelogs, Architecture Decision Records (ADR), and AI-optimized documentation. Use these templates to ensure your project documentation is standardized, scannable, and easily consumable by both human developers and AI agents.

Guides assistants through DOCX creation, editing, and redlining by describing when to unpack XML, use docx-js for new files, leverage the Document library for edits, and follow tracked-change workflows for reviews, including batching and precise OOXML edits.

Analyzes a codebase to surface subdomains (Core, Supporting, Generic) and recommend bounded contexts using DDD strategic design, helping teams clarify domain boundaries, cohesion, and service split decisions.

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

This advanced toolkit provides a comprehensive framework for in-depth financial analysis. It covers key functions including financial ratio analysis, Discounted Cash Flow (DCF) valuation, budget variance analysis, and rolling forecast construction. It is essential for strategic decision-making, investment analysis, and management reporting across various industries.