Agentic Coding

Set up MCP servers in Cursor to extend your IDE with external tools and data sources. Create a .cursor/mcp.json file in your project root or ~/.cursor/mcp.json globally, then add server configurations using the same JSON format as Claude Desktop. Cursor supports web search, GitHub integration, database access, browser automation, and file operations through MCP servers, enabling you to look up API docs mid-conversation, triage issues, inspect database schemas, and automate testing workflows without leaving the editor. Both project-level and global configurations are supported, and you can set up servers either manually by editing JSON or through the Command Palette. Key troubleshooting steps include enabling MCP in Cursor settings, verifying JSON syntax, checking the MCP output panel for errors, ensuring environment variables are properly inherited, and confirming remote server connectivity. Cursor and Claude Code share the same Model Context Protocol, so MCP server packages are interchangeable between them, though Cursor optimizes for IDE-bound work with full multi-cursor editing and inline suggestions, while Claude Code excels at terminal-based orchestration and handling many servers through lazy-loading tool search.

Agentic coding represents a workflow paradigm where AI agents autonomously drive the edit-test-fix loop, planning multi-file changes, executing tasks, and iterating until completion while developers direct and review. The 2026 landscape shows top coding agents evaluated on a 36-point agenticness framework measuring autonomy, reliability, and real-world complexity handling—Devin scored 17/36 as an adaptive collaborator integrating with Slack, Teams, Linear, and Jira to delegate migrations, refactors, and bug fixes with PR review. Key differentiators for advanced practitioners include codebase awareness across full repository indexing and multi-session context maintenance, integration depth spanning IDEs and CI/CD pipelines to minimize context switching, and operator sovereignty ranging from approval-required changes to independent multi-file refactoring. Best practices emphasize matching autonomy levels to risk tolerance and workflow needs, with open-source options now competitive with proprietary tools for self-hosted security and compliance requirements.

This repository provides a collection of AI agent skills designed for marketing tasks, built to work with Claude Code, Cursor, Windsurf, and other agents supporting the Agent Skills spec. The skills are markdown files that give AI agents specialized knowledge and workflows, structured hierarchically with product-marketing as the foundation skill that all others reference. Installation can be done via CLI (npx skills), Claude Code plugin, direct cloning, git submodule, or SkillKit for multi-agent environments. The v2.0 update consolidates and renames 17 skills, with migration guidance provided for upgrading from v1.x. Over 60 skills are available across categories including conversion optimization, content and copy, SEO, paid distribution, measurement, growth engineering, and sales enablement, each triggered contextually when users request help with specific marketing tasks or can be invoked directly with commands like /cro or /emails.

This tutorial demonstrates building a GitHub Agentic Workflow for CI failure triage using natural-language reasoning within bounded security guardrails. The workflow reads failed GitHub Actions runs, analyzes logs and jobs, and proposes diagnostic issues in staged mode for maintainer review before any repository write occurs. Key practices include layered security boundaries through permissions, read-only tools, network policies, and safe-output controls; structured prompting that treats all inspected data as untrusted and prefers noop over unsupported diagnoses; and staged rollout where proposed issues appear only in the workflow summary until explicitly promoted to trusted automation. The tutorial emphasizes compiling Markdown source into reviewable lock files, validating with strict checks, testing in sandboxes, monitoring cost through AI Credits, and maintaining maintainer control through multiple independent verification layers rather than relying solely on natural-language instructions.