The Personal AI Stack: A Power User's Guide

An AI tool like Claude Code gives you solid general-purpose capabilities out of the box. To make it truly indispensable, add the layers that teach it who you are, how you work, and what you do.

The Personal AI Stack is my seven-layer model for shaping a capable AI tool such as Claude Code around your projects, tools, and knowledge. I’ll walk through each layer, so you can choose which ones to add to your own setup.

The examples center on Claude Code, but you can adjust the stack to your own preferences.

I’ve been using the Personal AI Stack to expand and deepen my work. For example, it helped me ship a new version of REMnux with its MCP server and profile the RSAC Innovation Sandbox finalists. And my endpoint security startup guide and security product creation framework would’ve taken many more hours of browsing and note-taking without it.

Layer 1: Harness (Claude Code, Ghostty, Maestro)

The harness is the client AI software you use to interact with an LLM. Claude Code will be the tool I use as the basis for my examples. Other popular options include Codex, Gemini CLI, and OpenCode. Sometimes such tools are called AI agents or AI orchestrators; the terminology is ambiguous and overlapping.

You install the harness on your workstation and give it access to your local tools and files. That makes it much more capable than AI providers’ web-based chat interfaces.

Sign up for a Claude subscription, then install Claude Code. It’s a command-line tool, and this is the approach I recommend for technologists. If you don’t like using a terminal, you can download the Claude desktop app. Click its </> icon to use its built-in (but slightly hidden) Claude Code app.

If you’ll be using the command-line version of Claude Code on macOS or Linux, install Ghostty. It’s a better choice than the native terminal apps. You don’t need it if you’ll use Claude Code solely in the Claude desktop app.

If you find yourself running several Claude Code sessions at once, Maestro will launch and manage multiple Claude Code instances side by side. Think of it as a supercharged alternative to running them in Ghostty or the Claude desktop app.

By the way, don’t get hung up on the word “code” in the name Claude Code. It’s useful for any scenario where you want a customizable harness for Anthropic’s AI models.

Layer 2: Scaffolding (PAI, Skills)

Daniel Miessler’s PAI project amplifies Claude Code, making it smarter and attuned to your specific needs. Daniel describes PAI as a “context-based life operating system.”

As Anthropic improves Claude Code, it absorbs some of the capabilities PAI currently offers. Daniel keeps advancing PAI, staying a step ahead of what’s possible with Claude Code alone. For example, PAI gives Claude Code an adaptive approach to solving problems that Daniel calls The Algorithm, a method he designed to “hill-climb toward the ideal state using testable criteria.”

PAI includes Skills that extend Claude Code’s capabilities. For instance, the Council Skill pressure-tests your document, code, or idea from multiple perspectives. To do this, the Skill creates different personas with expertise relevant to your task, gathers their critique and ideas, and has them debate each other before unifying their perspectives.

When you run the PAI installer, it’ll ask you some questions about yourself. Don’t worry if you aren’t sure about the answers. It’ll be easy to adjust them later. For example, the installer asks you for an ElevenLabs API key, which PAI can use to speak with you; if you don’t need that feature, don’t bother with the key.

Beyond PAI, Skills offer additional ways of expanding the capabilities of Claude Code. For example, Anthropic publishes its official Skills, which include the ability to work with PDF and Microsoft Office files. Add them through Claude Code’s /plugin command.

Treat Skills like you’d treat any third-party software that might turn out to be malware. Only install Skills from trusted authors and sources.

Layer 3: Personalization (PAI Customizations)

PAI is meant to be an extension of you, which means it needs to know about your goals, tools, likes, and dislikes. This can feel personal, and that’s the intent. It’s what will allow Claude Code to become your Claude Code, so it can code, research, and write the way that works best for you.

PAI refers to its understanding of who you are as a “Telos,” which it captures in a series of markdown-formatted files. You can edit them yourself, but it’s easier to let Claude Code do that. Here’s a sample prompt you can give Claude Code for this. Replace [FILES] with paths to your resume, papers, notes, apps you’ve built, anything that captures how you think and work.

Help me set up my personal TELOS without overwhelming me. Use the Telos Skill. Start by reviewing these files for baseline context: [FILES]. Review silently, then interview me for 20-30 minutes, one question at a time, to populate only four files: MISSION.md (2-3 things my life is actually about), BELIEFS.md (5-7 specific beliefs, not platitudes), BOOKS.md (5-10 books that shaped my thinking, and why), and WRONG.md (3-5 things I used to believe but don't, and what updated me). Let the baseline guide what to ask, skip, and probe deeper. If I answer generically, push me for the specific story or stake behind it. Keep entries honest, not aspirational.

You can return to Claude Code later to work through the remaining Telos files. If you’re unsure what a file is for or how to approach it, ask it. You can also revisit your earlier Telos answers when life gives you something specific to record, such as a job role that changed, a goal that shifted, or a book that affected how you think.

Some of the Skills that come with PAI require API keys. For example, the Media Skill uses image-generation APIs to create illustrations and visuals. The Scraping Skill uses services such as Apify to access web content that would otherwise be hard to retrieve.

You can ask Claude Code to walk you through the process of setting up these keys based on your plans. Use a prompt like this:

Which PAI Skills need API keys? For each, explain what the Skill does, which API it uses, the approximate cost, whether there's a free tier, and why someone like me might or might not want it.

Layer 4: Hardening (Security Tweaks)

By default, Claude Code asks for approval before running most tools. PAI pre-approves most shell commands, file reads, and MCP tool calls, so you aren’t interrupted during normal work. It still requires confirmation for operations that can cause real damage, such as wiping a disk or force-pushing over a code branch.

Anthropic offers auto mode for tool approval, which uses an AI classifier at runtime instead of static rules. Its approach is compatible with PAI, so you can enable both if you want to experiment.

Trail of Bits published their recommended Claude Code configuration, which layers hardening on top of PAI’s defaults. If you don’t want to follow the guide yourself, point Claude Code at that repo and ask it to walk you through the options and recommend what’s worth applying based on how you work:

Review https://github.com/trailofbits/claude-code-config and walk me through the hardening options. For each one, explain the tradeoff and recommend whether I should apply it based on how I use Claude Code.

Trail of Bits settings worth paying attention to include:

• Block access to sensitive files: Prevents Claude Code from reading cloud provider credentials, package manager tokens, shell configuration files, and more.
• Disable auto-loading of project MCP servers: Stops cloned repositories from auto-registering MCP servers on your system, which protects against supply-chain attacks through malicious .mcp.json files.
• Disable telemetry: Stops Claude Code from sending operational data such as session IDs, account UUIDs, error reports, and feature flag states back to Anthropic.

AI agents can leak API keys and other secrets. The Trail of Bits hardening can block reads of common credential paths as a defensive layer. In addition:

• Consider using a vault that supplies secrets at runtime. 1Password Environments is one option to keep API keys out of your project folders.
• Review Anthropic’s API key best practices. Their guide covers spending limits per key, passing secrets via environment variables, and scanning your repositories for leaked secrets.

By the way, Claude Code adds itself as a co-author on every commit and pull request it helps you make. If you’d rather not advertise its involvement, whether for privacy, employer policy, or cleaner attribution, ask Claude Code to set the attribution field in ~/.claude/settings.json with empty strings for commit and pr.

Running AI agents creates many security concerns, such as prompt injection through files or web pages the model reads, and the model taking actions you didn’t intend. A deeper dive into that topic requires a separate article. The hardening above introduces some safeguards, but doesn’t cover the full threat model.

Layer 5: Tech Stack (Files, AI-Friendly Services)

Your tech stack determines how effective your AI will be. Start with the basics by organizing your projects in directories, one per project. To keep each project’s files under version control, use Git. It’s a system that works especially well for source code, but it’s also convenient for any text files.

An easy way to keep Git-organized files available is to store these projects in repositories on GitHub (or alternatives such as GitLab and Bitbucket). This lets Claude Code modify, track, and roll back your changes when necessary. Remember to tightly control access to your GitHub account (2FA is a must) and to set your non-public projects to be private.

Modern AI tools work best with text-based files, including Markdown, JSON, and YAML. An LLM can read, edit, and re-render these formats more precisely than Microsoft Word or Google Docs. You can still work with traditional formats, but workflows run more smoothly when your source content starts as plain text. Ask Claude Code to convert it into PowerPoint, PDF, or whatever your destination requires.

If you’ll be building software using AI, make sure the platforms and services you use are designed for programmatic interaction:

• AI-friendly infrastructure such as Cloudflare’s developer platform (Workers, Workers AI, R2, D1, etc.) gives you primitives that Claude Code can deploy and modify directly through APIs, MCP servers, and command-line tools. This is much more efficient than having your tools interact with a traditional VM via SSH or navigate a graphical user interface designed for humans.

• Services with clean, well-documented APIs let Claude Code do work that would otherwise require clicking through web dashboards. Examples include Resend for email, Stripe for payments, and Linear for project tracking. Choose tools that expose what you need as an API call.

Layer 6: Connectors (MCP Servers, CLIs)

MCP servers and command-line tools (CLIs) let Claude Code reach beyond local files into services that expand its capabilities and let it act on your behalf. MCP servers expose structured tools with their own authentication, while CLIs inherit your shell’s permissions and need to be trusted the same way as any local executable.

Anthropic offers ready-made connectors for services such as Google Drive, Gmail, Cloudflare, GitHub, Slack, and more. Authenticate one using the Claude website, and it becomes available in Claude Code automatically.

Beyond Anthropic’s managed connectors, MCP servers can also be added to Claude Code directly. SaaS vendors are starting to offer MCP-based access to their services.

Add MCP servers to Claude Code based on the services you want it to interact with, but make sure the services come from trusted individuals and companies, like you would with any software. For example, these MCP servers will help your AI agent search and access web content:

• Exa so Claude Code can search the web more effectively than using human-centric tools such as Google.
• Bright Data for accessing websites that block direct AI tool access; this is useful for PAI’s Research and Scraping Skills.

As an alternative to MCP, some services offer command-line tools that you install locally to let your AI agent interact with them. For example, Playwright CLI is designed to let your AI agent interact with the Chromium web browser bundled with this tool. PAI comes with Skills that tell Claude Code when and how to use it.

If you’d like to let Claude Code access your primary Chrome browser so it can use your authenticated sessions, enable Chrome’s remote debugging feature. There are several ways to “teach” Claude Code to interact with Chrome this way. The lightest is to install Petr Baudis’ chrome-cdp-skill; you can direct Claude Code to do that using a prompt like this:

Install https://github.com/pasky/chrome-cdp-skill as a Skill, in a way that lets a future session update it from the same source.

Be aware that this carries security risks, such as prompt injection from sites you visit. One mitigation is to give Claude Code a dedicated Chrome profile where you sign in only to sites it needs.

Look for MCP servers and CLI tools from trusted sources based on your work. For instance, if you’re using DigitalOcean, you’ll want to set up their MCP server. And maybe you’ll benefit from my own MCP server, which gives your agent access to hundreds of my blog posts as well as guidance for writing incident reports and evaluating product strategies.

Layer 7: Work (Your Projects)

Your past work is the most useful context you can give your AI, carrying your voice, decisions, and patterns. Point it at prior projects and documents when starting new ones, and the output will reflect your thinking. The more projects you’ve built, the richer that context becomes.

As you complete a project, direct Claude Code to capture details about it in a dedicated file, such as README.md, documenting your objectives, designs, and decisions. When starting a new project, refer your AI agent to your past work and your knowledge base so it starts strong and meets your expectations.

Also, consider creating a private knowledge base with your favorite books, frameworks, and reference materials that you want to make available to Claude Code as you work. This knowledge base can be a collection of documents stored as regular files. Or you can set it up as a local database, for instance, using the MCP Local RAG tool.

You, the Next Layer

The Personal AI Stack describes a set of layers that create a capable personal AI. The only missing layer is you. You’re the one who’ll take this setup from “Artificial Intelligence” toward “Actually Smart Intelligence.” Start building.