Skill UI

AWS CloudFormation enables Infrastructure as Code (IaC) for deploying AWS resources using YAML templates. It supports creating stacks, nested stacks, and managing multi-environment deployments. Features include change sets for safe deployments, drift detection, and rollback protection. Ideal for organizing large infrastructure and exporting values between stacks. Requires AWS CLI and IAM permissions.

This skill covers AWS CloudFormation template optimization, nested stack architecture, and production-ready patterns. It helps users write, review, and maintain infrastructure-as-code templates using YAML. Includes guidance on parameterization, drift detection, validation, and troubleshooting stack failures.

A structured playbook for improving the time and space complexity (Big-O) of existing, slow code. This skill guides developers through systematic refactoring, focusing on identifying bottlenecks like nested loops or N+1 queries. It emphasizes applying transformations one at a time, maintaining test coverage, and measuring performance gains to ensure correct and efficient code.

This skill helps users build Connection Auth Rules for Monte Carlo connections. It fetches connector schemas and transform steps from the apollo-agent repo. Users can configure credential transformation using Jinja2 templates and define transform steps like decoding certificates. It generates the ctp_config stored on the Connection object.

This skill covers strategies for compressing conversation history in AI agent sessions to manage context window limits. It focuses on optimizing tokens-per-task rather than just tokens-per-request. Techniques include anchored iterative summarization, opaque compression, and regenerative summaries. It also addresses artifact trail integrity and evaluation methods like probe-based testing to ensure critical information like file paths and decisions are preserved during compression.

A context preservation skill that creates snapshots and verifies data integrity before automatic compression. Protects critical project information from being lost during Claude Code's context window compression, ensuring zero data loss for complex technical projects with multiple skills and dependencies.

This skill specializes in advanced AI context engineering, managing dynamic information flows, vector databases, and knowledge graphs. It assists in building intelligent memory systems, optimizing context windows, and orchestrating multi-agent workflows. Ideal for enterprise-scale AI applications requiring coherent state management, RAG implementation, and semantic retrieval strategies to enhance system performance and reliability.

A comprehensive design guide for creating ultra-compact, high-utility user interfaces. Ideal for professional tools, financial dashboards, or IDEs where information density is paramount. Principles include compact layouts, monospace typography, high contrast dark themes, and maximizing functional pixels while minimizing whitespace.

Design scalable, reliable and cost-effective batch and streaming data pipelines. Covers ETL/ELT, Lambda, Kappa and Lakehouse patterns, Airflow/Prefect orchestration, dbt and Spark transformations, Delta Lake/Iceberg storage, data quality checks and cost optimization.

Master database schema and data migrations across popular ORMs like Sequelize, TypeORM, and Prisma. Learn rollback strategies, zero-downtime deployments, and schema transformations for safe database version upgrades and refactoring.

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

A skill for delegating bounded work to AI agents (Codex CLI, Pi, Claude Code, Hermes) while preserving context, ownership, and progress checks. Covers agent selection, prompt formatting for TUI agents, polling strategies, and remote VPS execution.