Skill UI

The comprehensive command-line interface (CLI) for interacting with all major features of GitHub. This tool allows developers to manage repositories, create and review pull requests, track issues, utilize Codespaces, and perform advanced organizational tasks—all directly from the terminal. It is essential for streamlining DevOps workflows and automating GitHub operations.

This comprehensive automation toolkit manages core Bitbucket functionalities, including repositories, workspaces, pull requests, and issues. It enables programmatic control over development workflows—from creating and reviewing pull requests to listing repos, tracking issues, and managing workspace members. Essential for integrating CI/CD pipelines and streamlining the entire software development lifecycle.

This tool automates the entire workflow of creating a GitHub Pull Request (PR) directly from a structured specification file. It analyzes the template, extracts requirements, checks for existing PRs, creates the draft, updates the body and title with specification details, and finally transitions the PR state to 'Ready for Review'. This significantly streamlines the feature request submission process, ensuring all necessary documentation and metadata are included.

This tool searches the current repository and lists all pull requests assigned to the user. It provides comprehensive details for each PR, including its purpose and scope. The output is highly functional, highlighting pending review status, detailing any failed checks (with suggested fixes), and offering to automatically request a Copilot review to maintain continuous development flow. Ideal for daily workflow status checks.

This skill guides developers through the structured completion of a feature branch. It enforces running comprehensive tests, detects the current Git environment (e.g., common repository vs. worktree), and presents clear options (merge, PR, retain, discard). It ensures safe integration, manages worktree cleanup, and enforces best practices for version control workflows.

This guide outlines a mandatory, evidence-based protocol for declaring any work complete, fixing a bug, or submitting a pull request. It strictly mandates that all claims of success must be supported by fresh, verifiable evidence, such as passing test outputs, successful build logs, or comprehensive checklist verification. It eliminates reliance on assumptions, gut feelings, or trusting proxy reports.

Configure robust CI/CD pipelines for Apify Actor builds and deployments using GitHub Actions. This guide covers setting up unit tests on pull requests, running deep integration tests against the live Apify API, and automating the production deployment process upon merge. Ideal for maintaining reliable, high-quality scraping and automation projects.

Automate the critical workflows of design systems by integrating Figma directly into your CI/CD pipelines. This tool allows you to automatically sync design tokens on a schedule, export assets upon pull requests, and validate design system consistency, ensuring design-code parity and preventing manual errors during deployment.

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 details setting up continuous integration and continuous deployment (CI/CD) pipelines for web scraping projects. It integrates Bright Data proxy testing into GitHub Actions, allowing developers to run both mocked unit tests (for quick checks) and live integration tests (verifying actual proxy connectivity) automatically upon code push or pull request.

This guide details how to use sophisticated coding agents (such as Codex, Claude Code, or Pi) through a structured bash workflow. It provides methods for complex development tasks, including building new features, refactoring large codebases, and reviewing pull requests (PRs). Key best practices covered include managing pseudo-terminals (PTY), defining focused working directories (`workdir`), and executing tasks in the background for robust, multi-step automation.

Establishes comprehensive Continuous Integration and Continuous Deployment (CI/CD) workflows for Fathom meeting intelligence. This setup includes unit tests using mocked transcripts to validate data parsing logic and robust integration tests that connect to the live Fathom API. It ensures the reliability of webhooks and data processing for real-time meeting summaries and action items upon every pull request or merge.