Skill UI

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

Outlines principles and practices for building secure contracts, covering threat modeling, vulnerability mitigation, and deployment readiness to help teams reduce risk when launching automated agreements.

This skill provides a comprehensive framework for the continuous self-improvement of makepad-skills. It outlines automatic skill triggering via hooks, context-aware skill routing, and a structured process for capturing new patterns, error solutions, and best practices. It ensures the entire skill library remains robust and up-to-date throughout the development lifecycle.

This guide outlines a systematic process for transforming any recurring technical pattern, complex debugging solution, or workflow into a standalone, portable skill. It details the multi-step extraction workflow, including pattern identification, structured documentation generation (SKILL.md), and quality control checks to ensure the skill can be reused across different projects.

This guide outlines best practices for creating high-quality, structured pull requests (PRs) using Git and GitHub CLI. It ensures that every PR description captures not only what changed, but also the 'why' and necessary context for reviewers, significantly improving the code review process and adherence to engineering standards.

This skill outlines a comprehensive methodology for designing, building, and deploying robust LLM-powered applications. It covers assessing task-model fit, designing modular pipelines (acquire -> prepare -> process -> parse -> render), and managing state using file systems to ensure scalability and reliability in AI systems.

Placeholder skill entry for risk modeling workflows that outlines basic analytics and scenario planning used to quantify potential losses and guide mitigation decisions.

This comprehensive guide outlines world-class product design principles, mimicking Apple's standard. It covers building robust design systems, defining essential design tokens (color, typography, spacing, motion), structuring UI components (atoms to organisms), optimizing UX flows, and designing engaging onboarding experiences. Ideal for product designers and PMs needing to establish a cohesive, scalable visual language and improve overall user experience.

This framework outlines the 'Hook Model' (Trigger, Action, Variable Reward, Investment) for building deeply engaging, habit-forming product loops. It guides designers in optimizing user retention, understanding behavioral psychology, and structuring product features to move users from deliberate usage to automatic, routine behavior. Use it for onboarding, feature design, and improving long-term user stickiness.

A comprehensive guide for building modern, accessible, and scalable user interfaces using the shadcn/ui component library. This document outlines core principles, best practices for component composition, styling rules (including Tailwind CSS usage), and structured approaches for forms and layouts. Essential for developers building complex design systems and ensuring code consistency.

This skill outlines the comprehensive process of memory forensics on compromised Linux systems. It guides users through acquiring volatile memory using the LiME kernel module and analyzing the resulting image with the Volatility 3 framework. Key artifacts extracted include process lists, network connections, bash history, and loaded kernel modules, making it an essential technique for incident response, threat hunting, and security investigations.

This skill outlines the development of an automated pipeline to ingest raw Indicators of Compromise (IOCs) from various sources. It performs normalization, defanging (e.g., converting 'http://' to 'hxxp://'), and converts indicators into the standardized STIX 2.1 format. The processed, safe indicators can then be securely distributed via TAXII feeds, MISP, and other threat intelligence platforms.