AI Summary
→ WHAT IT COVERS Remy Gaskell teaches beginners how to build AI agents using local markdown files, MCP tool connections, and skill files to automate entire business departments — moving beyond basic chat models toward a personal AI operating system that compounds productivity gains across weeks and months. → KEY INSIGHTS - **Chat vs. Agent Architecture:** Chat models operate on a question-to-answer loop requiring constant human input, while agents run a goal-to-result cycle using an observe-think-act loop. The agent continues iterating autonomously until predefined completion parameters are met, eliminating the need to babysit each step and enabling 10–20x productivity gains over time. - **agents.md Context Files:** Instead of relying on automatic cloud memory, agents require a manually created agents.md file (named claude.md in Claude Code, gemini.md in Gemini) that loads role, business context, and preferences at the start of every session. Build this file by having a chat model conduct an interview-style Q&A to extract relevant details. - **memory.md Self-Improving Loop:** Add a memory.md file alongside agents.md and instruct the agent to update it whenever corrections or new preferences are given. This creates a compounding improvement system where errors decrease over time. Keep claude.md under 200 lines and configure it to save only substantial corrections to prevent rule conflicts. - **MCP Tool Connections:** Model Context Protocol, created by Anthropic, standardizes how agents connect to external tools like Gmail, Google Calendar, Notion, Stripe, and Granola. Once connected via MCP inside any harness — Cowork, Claude Code, Codex, or Manus — the agent can execute multi-tool workflows, such as drafting a proposal email, creating a Stripe payment link, and setting up a Notion project simultaneously. - **Skill Files as AI SOPs:** Skills are markdown-based standard operating procedures stored in a .skill file that package repeatable processes so they never need re-explaining. Create them two ways: upload a process transcript and invoke the built-in skill creator skill, or complete a manual process once with the agent then ask it to generate the skill. Skills can be chained together and triggered on scheduled tasks. → NOTABLE MOMENT Remy Gaskell demonstrated live that after uploading a course transcript on viral hooks, a single prompt instructed the agent to package the entire course into a reusable skill file — meaning a multi-hour learning resource became an instantly invocable, repeatable content process without any manual formatting. 💼 SPONSORS None detected 🏷️ AI Agents, Model Context Protocol, Prompt Engineering, Workflow Automation, AI Productivity Tools