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Methodologies for the Optimization of Developers' Workflow with AI Agents
Learn to optimize AI developer workflows using Plugins, MCP servers, and Custom Skills. Move beyond vibe coding to become an AI Engineer, understanding context security and modularization.
This repository is an architectural and educational guide designed to enhance Artificial Intelligence agents through the implementation of Plugins, MCP servers, and Custom Skills, built upon an open‑source software that enables fast connections between MCP servers and OpenCode.
The workflow for software development has changed significantly with the arrival of artificial intelligence. However, the advances have been very rapid and numerous, making it difficult to keep up. For quite some time, I have observed that new tools and approaches constantly emerge, and most people —myself included— struggle to keep pace. Many resort to vibe coding, obtaining only average results.
In response to this phenomenon, I identified that there are different ways to improve the daily use of agents. That is why I created this repository, conceived from the perspective of a student for other students. The goal is to leverage the wide variety of agents with powerful free tiers and explore how to enhance them, whether free or paid, through different methodologies. The ultimate aim is to move beyond vibe coding and instead embrace a more solid practice: becoming an AI Engineer.
This repository documents technical integrations for terminal-based autonomous AI agents.
- MCPMCP is the open-source standard for securely connecting AI agents (like LLMs) to external tools, data, and enterprise workflows.The Model Context Protocol (MCP) functions as a standardized integration layer: think of it as a USB-C port for AI applications. Developed and open-sourced by Anthropic, this protocol allows large language models (LLMs) to access real-time context and execute actions via external tools like GitHub, Jira, or proprietary databases . It uses a simple JSON-RPC interface to define tools, schemas, and endpoints, which enables AI agents to perform complex, state-changing tasks—such as creating a GitHub issue or running a test script—rather than just generating text . MCP is essential for building agentic AI systems that can autonomously pursue goals and operate within defined safety and permission boundaries .
- SkillsThe essential platform for building in-demand AI and technical capabilities: access free courses, skill badges, and industry-recognized Google Cloud certifications.Skills (Google) delivers targeted, high-impact technical training to close the global skills gap. The platform offers extensive no-cost options for all learning levels, including hundreds of courses focused on AI and technical fundamentals. Users earn shareable digital credentials: complete a certificate learning path for a full certification, or finish a series of courses to secure a Skill Badge, proving practical, technical expertise. This is the direct path for individuals and Google Cloud partners to validate knowledge and advance their careers with industry-recognized credentials.
- autoskillsAutoskills automates technical talent assessment through real-world coding simulations and AI-driven performance analytics.Autoskills replaces static multiple-choice tests with dynamic environments where candidates solve production-level problems. The platform tracks 50+ granular metrics (including syntax efficiency, debugging speed, and logic flow) to generate a comprehensive developer profile. By integrating directly with GitHub and Slack, it cuts engineering interview hours by 60% while ensuring hires possess the specific architectural skills required for high-scale systems.
- NodeNode.js is a high-performance JavaScript runtime built on the V8 engine for executing scalable network applications.Ryan Dahl launched Node.js in 2009 to rethink server-side concurrency. It utilizes an event-driven, non-blocking I/O model to manage thousands of concurrent connections on a single thread. The system runs on Google's V8 engine (C++) and provides access to npm (a registry with over 2 million packages). Companies like Netflix and LinkedIn use it for its speed and scalability: it remains the top choice for real-time data streaming and microservices.
- Copilot SDKThe open-source framework for integrating context-aware AI agents and interactive Copilots into React applications.CopilotKit (the Copilot SDK) provides developers with a streamlined toolkit to build production-ready AI assistants. It bridges the gap between LLMs and application state through specialized React hooks like useCopilotReadable and useCopilotAction. By integrating these components, engineers can grant AI agents the ability to perceive live UI data and execute frontend functions directly. This SDK supports major providers including OpenAI, LangChain, and Anthropic, enabling features like in-app chat, autonomous text generation, and multi-agent orchestration with minimal boilerplate.