Skill UI

This skill provides a structured approach to monitoring and analyzing data leak sites (DLS) maintained by ransomware groups. By extracting structured data on victim postings, threat group tactics, and sector-specific details, users can assess current ransomware risk, track attack trends, and proactively build robust defense strategies.

This skill automates the entire phishing incident response workflow using the Splunk SOAR REST API. It parses suspicious email headers and bodies to extract artifacts (URLs, IPs, sender details), creates a structured incident container, triggers a comprehensive automated investigation playbook, and compiles a final verdict report for security teams.

This guide provides a structured approach to red-teaming Large Language Models (LLMs) to detect sensitive data leakage from system prompts. It simulates advanced attacks—including prompt injection, instruction override, and encoding tricks—to identify embedded secrets, API keys, and proprietary business logic. Essential for validating adherence to OWASP LLM07 and securing AI applications against data exfiltration.

Workthreads analyzes a team's coding history across multiple repositories to generate a structured weekly status report. It clusters raw logs into 'work threads' and automatically tags their lifecycle status (new, open loops, closed, reverted). This is essential for leads needing a high-level, actionable summary of project progress, outstanding tasks, and completed features without reading thousands of lines of raw activity.

This skill generates complete, production-grade client SDKs and API wrapper libraries for any REST API, regardless of the language (Python, TypeScript, etc.). It handles boilerplate code like authentication, retry logic (exponential backoff), data modeling, and structured resource management, saving significant development time. Use it whenever you need to consume a REST API via code.

This skill provides a comprehensive tech debt assessment, identifying, classifying, and prioritizing core maintainability issues. By drawing on classic engineering principles, it helps teams build a structured refactoring roadmap, optimizing code health and development velocity.

This skill provides comprehensive pull request (PR) code reviews that go beyond simple syntax checks. It aggressively surfaces deep-rooted decay risks, architectural design smells, and maintainability issues. Findings are reported using a structured Symptom → Source → Consequence → Remedy framework, drawing insights from classic engineering principles. Ideal for reviewing complex or crucial code modules.

This skill provides a comprehensive set of core UI components (e.g., Box, Button, Input, Card) and implements a structured design system. It guides developers on using design tokens (like `$textPrimary` or `$4`) instead of hard-coded values, ensuring visual consistency and scalability across applications. Ideal for any project requiring standardized, maintainable UI development.

This skill provides comprehensive best practices for designing modern and robust React user interfaces. It covers critical patterns for managing asynchronous data, including optimal loading state indicators (skeletons vs. spinners), structured error handling hierarchies, and designing contextual empty states. Essential for improving user experience during data fetching and operations.

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 skill guides the implementation of advanced SEO strategies tailored specifically for technical audiences and developers. It covers specialized techniques such as optimizing for error messages, researching high-intent technical long-tail keywords, creating structured how-to guides, and developing objective tool comparison content. Use this when targeting developers who search with precise technical intent, needing solutions that go beyond standard documentation.

This skill helps developers achieve maximum AI readiness by automatically detecting the project stack and seeding context using a single, precise goal sentence. It systematically fills the essential project context (the 6 Ws), requiring the user only to answer the few remaining knowledge gaps (like the 'why' or 'when'). This creates a portable, structured context file, ensuring AI models have a complete understanding of the project before coding, minimizing guesswork and maximizing efficiency.