Skill UI

This skill helps developers adhere to Test-Driven Development (TDD) principles by generating comprehensive, failing unit and integration tests. It is specifically designed for the 'Red' phase, ensuring that expected behaviors, edge cases (null, boundary values), and error handling are rigorously defined before any implementation code is written. It forces the definition of the 'what' before the 'how'.

This skill guides developers through advanced, safe, and systematic code refactoring using Test-Driven Development (TDD) principles. It provides a comprehensive workflow encompassing pre-assessment, detecting code smells, applying major design patterns (SOLID, Factory, Strategy), optimizing performance, and ensuring safety through rigorous, incremental testing. Use this when improving code structure, restructuring large components, or updating legacy systems while guaranteeing functional integrity and maintainability.

This skill provides a comprehensive framework for designing optimal team structures for early-stage startups. It covers role-by-role hiring plans, compensation benchmarks (salary and total compensation), and equity allocation strategies, guiding founders from Pre-Seed through Series A to ensure scalable growth and proper talent investment.

This skill generates comprehensive Technical Design Documents (TDDs), guiding users through structuring architectural decisions, implementation plans, and risk assessments. It follows industry best practices from standards like Google Design Docs, RFC patterns, and ADRs. It focuses on high-level contracts and architectural 'what' and 'why', rather than specific, low-level implementation code.

This skill provides expert guidance on creating, reviewing, and managing production-grade AWS infrastructure using Terraform. It focuses on best practices for reusable module design, robust remote state management (S3 + DynamoDB), and adhering to modern HashiCorp Configuration Language (HCL) patterns like using 'for_each'. Ideal for teams building or auditing complex, scalable AWS environments.

Provides Terraform and OpenTofu best-practice guidance for module design, testing selection, CI/CD pipeline setup, security scanning, and architecture decisions in production infrastructure-as-code projects.

This skill provides advanced expertise in Infrastructure as Code (IaC) using Terraform and OpenTofu. It covers designing robust, reusable modules, managing complex multi-cloud state backends, and implementing enterprise-grade CI/CD pipelines. It specializes in governance, Policy-as-Code, and achieving GitOps compliance for full infrastructure automation.

A specialized skill designed to ensure robust software quality by covering the entire testing lifecycle. This includes defining test strategies, writing unit, integration, and E2E tests, and implementing advanced testing types like performance (k6), security (OWASP), and functional testing. It guides users through coverage analysis, debugging flaky tests, and generating detailed defect and quality reports.

This skill maps identified security threats to appropriate controls and mitigation strategies. It is essential for prioritizing security investments, building comprehensive remediation roadmaps, validating existing control coverage, and performing detailed risk treatment planning. It helps organizations design robust, defense-in-depth security architectures.

A specialized skill for conducting comprehensive security architecture reviews, risk assessments, and threat identification. It masters advanced methodologies like STRIDE, PASTA, and attack tree construction. Use this to build secure-by-design systems, identify potential attack vectors, and strengthen system resilience before deployment.

This comprehensive skill provides advanced architectural patterns for implementing robust API throttling and rate limiting policies. It covers critical concepts like concurrency limiting, priority queuing, circuit breakers, and adaptive throttling that adjust limits based on real-time backend health metrics (p95/p99 latency). Use this when designing resilient, scalable backend services that must protect themselves from overload and maintain stability under high load.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.