Skill UI

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 guide sets up robust CI/CD pipelines for validating Attio integrations. It combines mocked unit tests (using MSW) for speed and gated live API integration tests for pre-release validation. Use this to ensure complete code reliability and automated quality checks across all development pushes.

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.

A comprehensive pre-production audit tool that scans a codebase for critical issues across eight dimensions: security, database, deployment, code quality, AI/LLM usage, dependencies, frontend, and observability. It intercepts deployment commands, forcing users to resolve critical failures before shipping code. The audit is stack-agnostic and provides detailed pass/fail/manual reports.

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.

This meta-skill executes three crucial quality gates—ruff (linting), mypy (type checking), and pytest (unit testing)—in parallel over the staged file differences. It aggregates the results to provide a single, definitive BLOCK or APPROVE verdict, ensuring code readiness and mirroring the strict checks enforced by CI/CD pipelines before local commits.

Luna serves as the final quality gate, rigorously reviewing code for objective correctness, security vulnerabilities, and overall reliability. It checks for critical flaws such as injection vulnerabilities, authorization bypasses, race conditions, and adherence to the architectural blueprint. It ensures that only high-quality, safe code progresses to QA and deployment.

This skill provides a deterministic, git-only method to verify claims made by AI agents (e.g., 'done', 'shipped', 'fixed'). Instead of trusting the agent's self-report, it audits the commit's diff and git history using the DOS kernel (`dos verify`, `dos commit-audit`) to confirm if the claimed change genuinely occurred and is backed by non-forgeable evidence. It acts as a critical quality gate.

A specialized quality gate skill designed to review complex, multi-source outputs generated by the CrossFrame framework. It rigorously checks reasoning fidelity, evidence boundaries, source anchors, and structural continuity. Use this when advanced verification of logical rigor and source integrity is required, ensuring outputs meet strict internal protocols and avoid common structural failures.

This skill acts as a comprehensive maintenance orchestrator for the brooks-lint plugin repository. It executes a rigorous, sequential subagent pipeline—including authoring, evaluation, QA, trigger auditing, and release management. Use this when adding, editing, or updating skills to ensure all related components (manifests, README, CHANGELOG) are synchronized and adhere to strict quality gates throughout the development lifecycle.