Skill UI

This advanced skill automates the enforcement of Python code quality using Ruff. It implements a controlled, iterative workflow that supports safe and unsafe automatic autofixes, formats the code, and reviews diffs after each pass. The process recursively repeats until all findings are resolved or require explicit user decision, ensuring high code consistency and quality gates.

A comprehensive tool for planning and auditing programmatic SEO pages generated at scale from structured data sources (CSV, JSON, APIs). It guides best practices for designing templates, defining scalable URL systems, establishing internal linking strategies (Hub/Spoke), and implementing robust quality gates. Use this when ensuring content uniqueness, preventing thin content penalties, and mitigating risks associated with large-scale, data-driven content generation.

Configure comprehensive CI/CD pipelines for integrating Grammarly's text analysis into your development workflow. This setup uses GitHub Actions to run unit and integration tests on pull requests, validating API connectivity, enforcing content quality gates (e.g., minimum grammar scores), and ensuring documentation quality before merging to the main branch.

This skill performs comprehensive, systematic cleanup across 11 critical dimensions of code quality, including dead code removal, security vulnerability checks, weak type elimination, and performance optimization. It analyzes code, assigns confidence scores, and verifies all suggested changes using build and test gates. Ideal for projects suffering from accumulated technical debt, or before major release cycles to ensure robust, maintainable code.

This skill provides integration with Grammarly's Writing Score API, enabling applications to automatically score documents. It evaluates writing quality across four dimensions: engagement, correctness, clarity, and tone. Use this when building content quality assurance features, implementing quality gates, or providing detailed, multi-dimensional feedback on written content.

This harness provides comprehensive, reproducible evaluation pipelines for complex LLM chains and agents (LangChain/LangGraph 1.0). It integrates golden dataset management, LangSmith evaluation, RAGAS metrics, deepeval LLM-as-judge, and structured agent trajectory analysis. Use it when establishing quality benchmarks for new chains, diagnosing performance regressions after model switches, or implementing CI/CD gates to prevent quality drops.

This framework enforces high output quality, intellectual honesty, and continuous improvement across all AppSec agents. It mandates structured processes, including self-critique loops, anti-rationalization guards, and self-reflection quality gates (≥8 scoring). Applicable during code review, SAST, SCA, and threat modeling, it ensures rigorous and verifiable security analysis.

This comprehensive guide establishes the standards for a modern, minimalist, and highly functional design system. It defines foundational elements like typography, color tokens, and spacing scales, while strictly adhering to WCAG 2.2 AA accessibility. It provides structured workflows, detailed component rules, and quality gates for both designers and engineers to ensure consistent, usable, and aesthetically pleasing product experiences.