Skill UI

This skill monitors the performance of Claude Code and the local operating system. It diagnoses slowdowns, measures CPU/RAM/Disk usage, checks API latency, and generates detailed system health reports. It helps determine if the bottleneck is local hardware or the remote API connection, providing actionable corrective suggestions to improve the development workflow.

This skill guides users on setting up automated code quality gates directly at the Git level. It covers best practices using frameworks like Husky, lint-staged, and pre-commit. Users can enforce linting, formatting, type-checking, and standardized commit message conventions (e.g., Conventional Commits) before code is committed or pushed, significantly improving code hygiene and development workflow consistency.

This skill enforces a mandatory, structured gate for starting new implementation tasks. It mandates that all work must be tracked via a GitHub issue, requiring explicit, testable acceptance criteria before any execution can begin. It defines a strict workflow (Problem, Goal, Scope, Acceptance Criteria, Status) to manage task readiness, ensuring that development only proceeds when criteria are fully defined and verifiable.

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

This guide details the systematic process of creating comprehensive incident response playbooks and runbooks. It covers structured procedures, decision trees, RACI matrices, and escalation protocols for various incident types (e.g., ransomware, data breach). Designed for maturing an Incident Response (IR) program, maintaining compliance (SOC 2, HIPAA), and integrating automated workflows into SOAR platforms, aligning with NIST SP 800-61r3 standards.

This guide provides a comprehensive workflow for Test-Driven Development (TDD) within Laravel applications. It covers best practices for writing unit, feature, and integration tests using modern tools like Pest and PHPUnit. Learn how to manage database states, mock external services, and achieve high code coverage efficiently.

This skill is designed for implementing and managing user verification workflows within a Laravel PHP framework. It facilitates processes such as email confirmation, password reset validation, and ensuring data integrity for secure application access. It is essential for robust web backend development.

Master the use of the `gh` CLI to interact with core GitHub features. This guide covers managing issues, checking Pull Request statuses, listing and debugging CI workflow runs, and executing advanced data queries via the GitHub API. Essential for automating DevOps workflows and debugging CI/CD pipelines directly from the terminal.

This guide enforces the 'spec-first' principle in software development, treating the specification as a non-negotiable contract. It outlines a rigorous, multi-stage workflow detailing mandatory sections (Functional/Non-Functional Requirements, Acceptance Criteria, API Contracts, Data Models) and bounded autonomy rules. Users learn how to define the system 'must do' before writing a single line of code, thereby minimizing rework and eliminating scope creep.

This skill guides developers on building LLM-powered applications using the Claude API, Anthropic SDKs, or the Agent SDK. It provides comprehensive guidance on language detection, best practices (like default model versions and streaming), and choosing the correct development surface (API call, Workflow, or Agent) based on the use case.

Summarizes the Test-Driven Development workflow with RED/GREEN/REFACTOR phases, the AAA pattern, prioritization cues, anti-patterns, and AI-augmented agent roles to guide implementation.