Skill UI

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

Configures robust CI/CD pipelines for Vercel projects using GitHub Actions. This guide automates the full deployment lifecycle, including generating preview environments on every pull request, performing reliable production builds upon merging to main, and implementing test gating to ensure code quality before release. Ideal for professional web development workflows.

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 AI-assisted capabilities for creating and managing Continuous Integration/Continuous Deployment (CI/CD) workflows using GitHub Actions. It can generate new workflows from project requirements, optimize existing pipelines, debug failures, and implement industry-standard security best practices, streamlining the entire development and deployment process.

Audits GitHub Actions workflows that invoke AI coding agents to surface prompt injection and sandbox risks, tracing attacker-controlled inputs through env vars, reusable actions, and workflow references before deployment or code review.

Generates practical and robust CI/CD pipelines by programmatically detecting the project's entire technology stack (language, runtime, tooling). It provides fast baseline setup for GitHub Actions or GitLab CI, ensuring repeatability, environment awareness, and alignment with project dependencies. Ideal for onboarding new repositories, standardizing DevOps workflows, or migrating existing deployment processes.

Performs static security analysis on GitHub Actions workflows that incorporate AI coding agents (e.g., Claude, Gemini, OpenAI). This skill helps identify critical data flow vulnerabilities, such as where attacker-controlled input can reach AI agent prompts or internal systems via CI/CD pipelines, going beyond simple expression checks.

A rigorous framework designed to audit GitHub Actions workflows for exploitable security vulnerabilities. This skill focuses strictly on real-world attack patterns, requiring every identified finding to include a concrete exploitation scenario (Entry Point, Payload, Impact). Ideal for securing CI/CD pipelines against external attackers.

A comprehensive guide and template for deploying applications from idea to production. It covers modern DevOps practices, including containerization with Docker, robust CI/CD workflows using GitHub Actions, and infrastructure management with AWS SAM and Terraform. Includes setup for monitoring, health checks, and scalable cloud deployments.

Build a production-ready, AI-powered data collection agent for any public source (job boards, prices, news, etc.). This agent automatically scrapes data, enriches it using free LLMs (like Gemini Flash), and stores the results in databases such as Notion, Sheets, or Supabase. It runs on a free schedule using GitHub Actions and learns from user feedback, making it ideal for continuous data monitoring and tracking.

This tool scans GitHub Actions workflows and CI/CD configurations to identify critical supply chain attack vectors. It specifically checks for unpinned actions, risks from script injection using expressions, dependency confusion, and secrets exposure, helping organizations harden their build systems against sophisticated threats.

This skill guides the implementation of Gitleaks for robust secret scanning across development lifecycles. It covers setting up pre-commit hooks to prevent secrets from being committed, integrating scanning into CI/CD pipelines (like GitHub Actions), and writing custom regex rules to detect and remediate hardcoded credentials (API keys, passwords, tokens) in Git history, ensuring adherence to secure SDLC practices.