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Agentic Local SEO Masterclass 1 | Claude Code, Codex, and Context Engineering
M Shahid AI Automation
Overview
This masterclass introduces the evolution from prompt engineering to context engineering for AI agents, emphasizing the importance of providing clear context for better AI outputs. It delves into 'skills' as standardized instructions for AI agents like Claude, Codex, and CoWork, enabling automation and specialized tasks. The session demonstrates how to build and utilize these skills, particularly for local SEO, by integrating them into workflows. It also highlights the role of 'MCPs' for server communication and 'context files' for knowledge bases, showcasing practical examples of automated workflows that can generate reports, perform competitive analysis, and manage content creation, all while emphasizing local storage and efficiency.
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Chapters
- The field is shifting from basic prompt engineering to more sophisticated context engineering.
- Providing clear and comprehensive context is crucial for AI Large Language Models (LLMs) to deliver satisfactory results.
- Insufficient context leads to suboptimal AI outputs, making users feel the AI is not performing well.
- The focus will be on skills and sub-agents rather than just basic prompting.
Understanding this shift is essential for leveraging AI effectively, as it moves beyond simple commands to more nuanced and context-aware interactions.
Users often feel unsatisfied with AI results because they haven't provided enough context, indicating a need for better context engineering.
- Skills are standardized instructions for AI agents (like Claude, Codex, Gemini) that enable them to perform specific tasks.
- Just as humans have unique skills, AI agents can be equipped with skills to become specialized and efficient.
- Skills allow for the automation of complex workflows, mirroring human processes like keyword research.
- Sub-agents function like a team, with a project manager overseeing individual agents performing specific tasks.
Developing and utilizing AI skills allows for the creation of powerful, automated agents that can handle complex tasks, significantly boosting productivity.
An agent with a 'GBP optimization' skill can be recognized and sought after for its ability to rank Google Business Profile listings, similar to how a human expert is known for a specific skill.
- The session recommends using Claude Code, CoWork, or Codex over the standard Claude chat interface for more advanced capabilities.
- These tools allow for the combination of technical (Claude Code, Codex) and non-technical (CoWork) approaches to automation.
- Skills are stored in specific file formats, such as `.cloud` for Claude or `.codex` for Codex.
- These platforms facilitate the creation of automated workflows by integrating various skills and tools.
Choosing the right AI platform and understanding its specific tools is key to unlocking advanced automation and workflow creation capabilities.
Combining Claude Code and Codex allows technical users to build complex workflows, while CoWork provides a more accessible interface for non-technical users to automate tasks.
- AI skills have two main parts: a name and a description, acting like a headline or metadata for the AI.
- Skills should be concise, ideally under 500 lines, and structured using bullet points or short sentences for optimal AI processing.
- Skills can include internal linking to other files (e.g., `gap_analysis.md`) to reference related information and avoid overly long skill files.
- Skills can be designed to output results in various formats, such as CSV or PDF files, directly to your local system.
Properly structuring AI skills ensures that the AI can efficiently understand and execute complex instructions, leading to more accurate and useful outputs.
A 'Competitive Analysis' skill can be built to automatically gather data, analyze competitors, and generate a CSV or PDF report, all within a single workflow.
- Context files provide AI agents with a knowledge base, descriptions, and memory, guiding their behavior.
- MCPs (Model Communication Protocols) simplify server-client communication, allowing AI to interact with external services like Semrush or Google Profiles without complex coding.
- These protocols use connectors and apps to enable direct AI interaction with platforms like Figma or Canva.
- Providing data in well-structured formats, especially Markdown (.md) files, enhances AI readability and processing speed.
Understanding context files and MCPs is crucial for enabling AI agents to access and process external information and interact with other services seamlessly.
Using MCPs, an AI agent can directly interact with Figma to create designs or Canva to generate graphics, based on the provided context and instructions.
- Workflows can automate entire processes, from initial research to content creation and website building.
- AI tools can create folder structures, generate files (like `.md` or `.csv`), and manage data locally on your system.
- This local processing eliminates the need for manual copy-pasting and reduces reliance on external platforms.
- Workflows can be continuously updated and optimized over time based on performance and new information.
Automated local workflows significantly enhance efficiency and productivity by handling repetitive tasks and managing project data directly on the user's machine.
A single command like 'SEO Research' can trigger a workflow that performs keyword research, competitor analysis, content brief generation, and entity extraction, all locally.
- The `cloud.md` file acts as a gateway or homepage for your AI project, providing an overview and directing the AI to relevant files.
- Keeping `cloud.md` concise (under 60 lines) prevents filling the AI's context window, allowing it to focus on essential information.
- The AI reads `cloud.md` at the start of each new session to understand the project context.
- The `agents.md` file is a copy of `cloud.md` used for compatibility with other tools like Codex, ensuring consistent instructions across platforms.
These central files are critical for managing AI project context, ensuring consistency, and enabling AI agents to navigate and process information efficiently across different tools.
When starting a new session, the AI reads `cloud.md` to get a high-level understanding of the project, then uses it to navigate to specific skill files or data files as needed.
- AI tools can automatically generate complex folder structures and populate them with relevant files.
- Using multiple AI instances simultaneously allows for parallel processing of different tasks, drastically increasing efficiency.
- This multi-instance approach treats AI agents like a team, assigning different tasks to each instance for parallel execution.
- The ultimate goal is to automate complex tasks like keyword research, content generation, and data analysis, integrating them into comprehensive workflows.
Leveraging multiple AI instances and sophisticated workflows transforms AI from a simple tool into a powerful, parallel processing engine for complex projects.
Running five AI instances concurrently, each handling a different phase of a project (e.g., keyword research, competitor analysis, content creation, data validation), significantly speeds up the overall process.
Key takeaways
- Effective AI interaction requires moving beyond basic prompts to sophisticated context engineering.
- AI skills are standardized, reusable instructions that empower agents to perform specialized tasks and automate complex workflows.
- Tools like Claude Code, CoWork, and Codex offer advanced capabilities for building and managing these automated workflows.
- Structuring AI skills concisely and using internal linking improves AI processing efficiency and accuracy.
- Context files (`cloud.md`, `agents.md`) are vital for managing project context and ensuring consistent AI behavior across different tools.
- Automated workflows, especially those processed locally, can dramatically increase productivity by handling repetitive tasks.
- Leveraging multiple AI instances simultaneously allows for parallel task execution, significantly accelerating project completion.
Key terms
Context EngineeringPrompt EngineeringAI SkillsAI AgentsClaude CodeCoWorkCodexMCPs (Model Communication Protocols)Context FilesWorkflowsLocal Storagecloud.mdagents.mdLLM (Large Language Model)
Test your understanding
- What is the primary difference between prompt engineering and context engineering in AI?
- How do AI skills contribute to the automation of complex tasks?
- Why is it recommended to use tools like Claude Code or Codex over standard chat interfaces for advanced AI applications?
- What is the role of `cloud.md` and `agents.md` files in managing AI projects?
- How can using multiple AI instances simultaneously enhance productivity?