Skill UI

This skill guides the integration of Windsurf workflows into CI/CD pipelines (like GitHub Actions). It provides comprehensive mechanisms for validating configuration files and enforcing AI code quality standards. Use it to automate policy checks, detect hardcoded secrets, ensure sufficient test coverage for new files, and manage complex config deployments across repositories, ensuring highly reliable and compliant code merges.

This skill provides comprehensive, AI-assisted linting configuration for ensuring high code quality standards. It assists with setting up and integrating major linting tools (such as ESLint, Prettier, and Stylelint), helping resolve complex configuration conflicts, suggesting best practices based on project patterns, and fully automating code quality enforcement across the development lifecycle. Use this when establishing or refining coding standards for a project.

This policy set establishes comprehensive guardrails for using AI coding assistants (like Windsurf/Cascade). It governs terminal commands, enforces workspace isolation (preventing access to secrets/credentials), and integrates CI/CD gates for code quality. Use this when setting up team development standards, mandatory code review policies for AI output, or implementing secure development pipelines.

A comprehensive guide to establishing production-ready coding standards and context for the Windsurf AI environment. Learn how to utilize root-level rules (.windsurfrules), granular workspace rules, Model Context Protocol (MCP) server integration, and advanced prompt engineering to guide the AI and maintain consistent code quality across projects.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

Documents how to build autonomous Claude Code loops, covering sequential pipelines, NanoClaw REPL, infinite multi-agent deployments, RFC-based DAG orchestration, and PR-driven CI cycles so teams can run persistent, quality-gated development workflows without babysitting.

Plankton enforces Claude Code writing-time quality by running formatters and linters on every edit, generating Claude subprocesses to repair remaining violations, and shielding linter configs and package managers across Python, TypeScript, Shell, and documentation.

Explains when to call the code-reviewer subagent, how to collect SHAs, fill the template, and act on its feedback so every task, feature, or merge meets quality expectations.

This skill simulates a senior engineer's final, deep audit of an entire codebase before committing or pushing to a remote repository. It systematically checks for junk files, dead code, security vulnerabilities (secrets, injection flaws), and performance bottlenecks (N+1 queries). Use this pre-flight check before any major release or public deployment to ensure maximum code quality and production readiness.

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

This comprehensive reference guide for Makepad provides essential materials for developers. It covers common errors, debugging tips, API documentation, and advanced techniques for adaptive and high-quality layouts. Use this resource when troubleshooting, looking up specific API details, or ensuring code robustness across different platforms.

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.