Skill UI

This workflow helps developers quickly build throwaway prototypes to answer specific design or functionality questions. It can generate interactive terminal applications to test complex state/business logic, or create multiple radically different UI variations toggleable from a single view, allowing for rapid validation of ideas before committing to production code.

This skill guides the systematic triage of project issues and external pull requests (PRs). It moves development items through a defined state machine using roles (e.g., bug, enhancement) and states (e.g., ready-for-agent, wontfix). The process involves context gathering, cross-codebase verification, deep questioning (grilling), and generating comprehensive, agent-ready briefs or marking them for human implementation.

This guide outlines a comprehensive workflow for detecting and preventing the exposure of sensitive AWS credentials (access keys, session tokens) within source code repositories, CI/CD pipelines, and configuration files. It utilizes TruffleHog for deep historical scanning and integrates git-secrets for robust pre-commit hooks, significantly reducing the risk of credential theft and unauthorized account access.

This skill simulates advanced red-teaming techniques targeting Microsoft Entra ID's OAuth 2.0 device-code flow and illicit consent mechanisms. It demonstrates how attackers can steal access and refresh tokens, bypass Multi-Factor Authentication (MFA), and pivot across various Microsoft 365 services. It is designed for authorized security testing to validate token protection and conditional access policies.

A specialized diagnostic skill designed to analyze failing GitHub Actions workflow logs. It identifies the root cause of crashes, such as missing secrets, environment mismatches, or deprecated syntax. The output provides precise YAML or code diffs required to fix the pipeline and optimize CI/CD processes.

This skill structures a highly controlled, bounded development life cycle for AI agents. It mandates a structured three-phase workflow: Planning (Spec), Implementation (Build), and Verification (Review). It is essential for building complex features from scratch, especially when dealing with ambiguous requirements, high risk, or needing explicit human approval gates to ensure the resulting code meets all defined specifications.

Provides comprehensive guidelines and utilities for modern unit testing using Jest and React Native. Covers Test-Driven Development (TDD) workflows, Behavior-Driven Development (BDD) principles, advanced mocking strategies for modules and GraphQL hooks, and implementing reusable factory functions for props and data. Use this skill to write robust, maintainable, and highly structured unit tests.

This powerful skill provides an interactive debugger interface, allowing developers to set breakpoints, step through source code line by line, inspect live variable states, and trace the entire call stack. It is essential for diagnosing complex bugs, understanding unexpected program flow, or determining the root cause when standard logging fails. It supports various languages via the DAP protocol.

This skill generates a detailed, step-by-step, interprocedural execution trace for a given piece of code and specific input scenario. It resolves name ambiguities, tracks type transitions, and highlights non-obvious behaviors (like implicit coercions or side effects). Use it when a user is confused about how code behaves, allowing for a deep dive into the actual execution path to clarify assumptions versus reality.

This library provides a comprehensive framework for managing bounded AI workflows (loops). Users can analyze existing codebases or work histories to discover opportunities for repeating, automated agent processes. It guides the user through finding published templates, auditing and repairing weak agents, adapting parameters, or crafting entirely new, robust automation designs, ensuring clear triggers, verification, and mandatory stopping conditions.

This skill guides the entire lifecycle of repeatable AI agent loops. Use it to discover recurring work patterns within codebases, find established solutions in a library, or interview users to craft bounded, effective loops. It supports auditing, repairing, running, and preparing AI workflows for publication, treating the loop as a robust feedback system.

This skill provides a structured, multi-step workflow for resolving conflicts encountered during in-progress Git merges or rebases. It guides users through analyzing conflict origins, preserving original development intents, resolving conflicting code hunks without introducing new behavior, and executing comprehensive automated checks (typechecking, testing, formatting) to ensure the resulting codebase is stable and functional.