Skill UI

Manages the deployment of local applications and websites to the Vercel platform. This skill intelligently detects the project's current state (e.g., if it is already linked or if git is available) and guides the user through the optimal deployment method. It prioritizes establishing a permanent git-push connection to facilitate a seamless Continuous Integration/Continuous Deployment (CI/CD) workflow. Use when the user explicitly requests deployment actions.

This tool automates complex Git workflows to save time and reduce manual overhead. It intelligently detects and groups related code changes, automatically generates descriptive branch names (e.g., fix/auth-bug), and streamlines the entire cycle: staging, committing, pushing, and creating Pull Requests (PRs). Use it to maintain clean, consistent commit histories efficiently.

This tool helps users manage and address review comments on open GitHub Pull Requests (PRs). It interacts with the GitHub CLI (gh) to fetch comments, guides the user through selecting which comments need fixes, and then applies the necessary changes, ensuring a structured approach to PR review.

This skill automates the debugging and fixing of failing Continuous Integration (CI) checks on GitHub PRs. It uses the GitHub CLI (gh) to locate failures, fetch detailed logs from GitHub Actions, summarize the failure context, and propose a structured fix plan. After user approval, it can implement the necessary code changes and verify the fix, streamlining the development cycle.

This skill provides a direct, fast shell invocation to capture the current git diff (staged, working-tree, or specific file list) as raw text. It is designed to replace slower LLM agent steps that merely execute 'git diff' commands, significantly speeding up CI/CD workflows and meta-steps that require repository state analysis.

A comprehensive guide to using the GitHub CLI (`gh`) for programmatic interaction with repositories. This tool allows users to list, manage, and review Pull Requests (PRs), create and triage issues, check CI/workflow status, and execute advanced API queries directly from the terminal, making it essential for scripting and automated DevOps workflows.

This meta-skill automatically inspects open GitHub Pull Requests, failing CI runs, and new issues. It utilizes the GitHub API to gather critical updates and summarizes the findings into three actionable buckets (To Review, Awaiting Me, CI Red). Finally, it persists follow-up tasks to long-term memory, helping developers efficiently prioritize their daily workflow.

This meta-skill automates the process of handling a GitHub issue by fetching context, delegating the fix to a sub-agent, summarizing the resolution, and finally opening a pull request (PR). It is best suited for small, well-scoped issues with clear acceptance criteria. This pipeline simulates a complete development workflow.

Use the Composio CLI to automate the entire Pull Request review and Continuous Integration (CI) triage process for GitHub and GitLab. This tool fetches code diffs, analyzes failure logs, posts detailed inline review comments, and iteratively patches and re-runs CI checks until the code passes, significantly enhancing code quality and developer efficiency.

A utility for managing and installing specialized AI skills into the Codex environment. It supports fetching curated skills from predefined sources (like openai/skills) or installing custom capabilities directly from any GitHub repository, including private ones. It handles listing available skills, installing specific modules, and provides robust fallback methods (download/git sparse checkout).

This skill provides a robust method for uploading local digital assets, including images, mockups, and full HTML files, to a Stitch project. It bypasses the limitations of standard tool calls by streaming files directly over HTTP, ensuring that large visual assets or complex web pages can be processed accurately for your design and prototyping workflow. Ideal for integrating local design iterations into cloud-based prototypes.

This workflow systematically analyzes deployed AWS resources and Infrastructure-as-Code (IaC) definitions. It collects usage metrics from CloudWatch and Cost Explorer to identify over-provisioning, unused resources, and inefficient configurations (e.g., right-sizing EC2, S3 lifecycle policies). It generates actionable cost optimization recommendations and creates structured GitHub issues for tracking and implementing savings.