vercel-deploy
Deploy applications to Vercel instantly. Supports preview and production deployments, automatic framework detection, and fallback script execution for seamless project publishing.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
213 skills found
Deploy applications to Vercel instantly. Supports preview and production deployments, automatic framework detection, and fallback script execution for seamless project publishing.
Automated quality gate using 5 parallel AI agents to review code changes for correctness, style, and consistency.
A comprehensive guide and reference for building, orchestrating, and deploying AI agents using the Google Agent Development Kit (ADK).
GoHighLevel workflow automation expert. Integrates with Hylo GHL API to manage workflows, API endpoints, UI navigation, and automation planning.
Specialized IDF (Information Display Frame) sub-agent for generating and reviewing CQRS Query Side implementations across Java, TypeScript, and Go.
A comprehensive library of 305+ modular instruction packages, Python CLI tools, and agent workflows designed to extend the capabilities of AI coding assistants like Claude Code, Cursor, Aider, and Gemini CLI.
Optimize developer experience for multi-component solutions: standardize onboarding, inner-loop, debugging, and cross-platform setup to eliminate friction and tribal knowledge.
A unified Solana development skill hub featuring multi-agent orchestration, progressive skill loading, and deep integrations for Anchor, Token-2022, DeFi protocols, and security auditing.
Manage isolated LlamaFarm development environments using git worktrees for parallel agent sessions and service testing.
Scaffold complex, multi-step coding tasks into actionable implementation plans and execute them autonomously using a Claude-driven bash loop.
Execute implementation plans using isolated subagents for each task, featuring a rigorous two-stage review process for spec compliance and code quality.
A meta-skill for building robust AI agent skills using a TDD approach: define failure (RED), implement the skill (GREEN), and plug rationalization loopholes (REFACTOR).