Skill UI

A comprehensive tool designed to audit and validate the structural integrity of a Domain-Driven Design (DDD) codebase. It detects cross-context import violations, checks for violations of aggregate invariant rules, and enforces naming conventions for domain events and repositories. Ideal for CI/CD gates and pre-merge code reviews to prevent architectural decay.

A comprehensive guide detailing security patterns and audit checklists for Automated Market Maker (AMM) smart contracts in DeFi. Covers critical vulnerabilities such as flash loans, slippage, reentrancy attacks, price manipulation, and integer overflow/underflow. Essential for reviewing major DeFi protocols like Uniswap.

A deterministic 'Stop hook' designed to enforce session hygiene for AI agents. It checks machine-verifiable facts such as file modification timestamps of learning logs, disk space, and predefined rationalization patterns. This gate ensures that complex tasks lead to captured knowledge, acting as a mechanical safeguard complementing AI self-auditing.

This comprehensive tool scans all project dependencies against databases of known CVEs (Common Vulnerabilities and Exposures) and security vulnerabilities. It is essential for conducting thorough security audits of third-party packages, ensuring code quality before major releases, or responding to specific security advisories. It provides actionable reports categorized by severity.

A comprehensive guide covering modern application deployment strategies, including Blue-Green, Canary, and Rolling deployments, ensuring zero downtime. It also provides deep dives into Docker multi-stage builds, container best practices, and setting up robust CI/CD pipelines using tools like GitHub Actions.

This guide outlines the architecture and implementation steps for building a robust, production-ready design system. It covers core elements like design tokens, reusable components, and layout patterns. Ideal for teams needing to maintain design consistency, ensure accessibility, and synchronize design (Figma) with implementation (Storybook).

This tool analyzes Git diffs to provide comprehensive insights for Pull Requests (PRs) and large code changes. It calculates risk scores based on churn history and sensitive areas, classifies changes (e.g., feature, bugfix), recommends required reviewers, and provides detailed statistics. Use this before merging to assess code quality and minimize deployment risk.

This comprehensive tool guides developers through the entire plugin ecosystem, helping them find and recommend the optimal plugins for any given workflow or task. It covers core coordination, multi-agent collaboration, advanced memory management (Vector DB, RAG), architectural design (ADR/DDD), security auditing, and modern development methodologies. Use it when starting a new project or optimizing existing development pipelines.

This guide outlines best practices for building scalable and maintainable Django applications and APIs. It covers recommended project layouts, implementing separated configuration files (dev/prod), advanced ORM techniques, and integrating middlewares, caching, and signals for enterprise-level robustness. Ideal for professional backend development.

A comprehensive guide covering essential security best practices for Django applications. It details how to implement robust authentication, granular authorization (RBAC), and crucial security settings like CSRF/XSS protection, HSTS, and secure environment configuration to prevent common vulnerabilities in production deployments.

A comprehensive guide to implementing Test-Driven Development (TDD) for Django projects. This skill covers the full Red-Green-Refactor cycle, utilizing pytest, pytest-django, and factory_boy to write robust unit and integration tests for Django models, views, and DRF APIs. Essential for developers aiming to build large-scale, high-quality Python applications.

Manages and coordinates complex workflows involving multiple AI agents and tasks. It efficiently distributes work across multiple workers, handles runtime errors, and intelligently merges disparate results into a unified output, making it ideal for large-scale, distributed AI computations.