Roger

Roger is a local, AI-powered career assistant that runs in AI coding tools (Gemini CLI), designed to automate the tailoring of high-quality resumes and cover letters.

"Who are we today?"

Roger is named after Roger Smith, the alien from the animated sitcom American Dad!.

Roger is famous for his ability to craft completely distinct, fully realized personas for any situation. He doesn't just "change clothes"; he constructs an entire backstory, personality, and set of credentials to fit a specific narrative. Whether he needs to be a hard-nosed detective, a wedding planner, or a corrupt politician, Roger has a persona for it.

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Table of Contents

• Prerequisites
• Installation & Setup
• Quick Start
• Critical Concept
• Features
• Project Structure
• Customization
• Troubleshooting

Prerequisites

Before running the agent, ensure your environment is fully prepared:

1. Gemini CLI: Installed and authenticated with your Google account. This is the interface where you will chat with the agent.

   • Installation Guide

2. Python 3.11+: Required to run the local MCP Server script. The server acts as a bridge between the AI model and your local file system.

3. uv: Modern, fast Python package manager for managing dependencies. This project uses uv instead of traditional pip.

   • Installation: curl -LsSf https://astral.sh/uv/install.sh | sh
   • Or via Homebrew: brew install uv

4. LaTeX Environment: A working local installation of pdflatex or xelatex. The agent relies on this engine to render the final PDF. (Detailed installation instructions in the next section)

5. Google Gemini API Key: Exported in your shell environment (GEMINI_API_KEY). This allows the CLI to communicate with the Gemini models.

Installation & Setup

1. Fork and Clone the Repository

Click the "Fork" on top-right of the page, and create your forked repo.

git clone https://github.com/yourname/Roger.git
cd Roger

2. Install LaTeX Distribution

The agent requires a LaTeX compiler to generate PDFs. Choose the installation method for your operating system:

macOS

Option A: Full Installation (Recommended)

brew install --cask mactex

This installs the complete MacTeX distribution (~4GB). After installation, you may need to add LaTeX to your PATH:

export PATH="/Library/TeX/texbin:$PATH"

Linux (Ubuntu/Debian)

Full Installation:

sudo apt-get update
sudo apt-get install texlive-full

Windows

Option 1: Download from miktex.org Option 2: Download from tug.org/texlive

Verify Installation: After installation, verify LaTeX is working:

pdflatex --version

You should see version information for pdfLaTeX.

3. Install Python Dependencies and Start MCP Server

Navigate to the MCP server directory and use uv to install dependencies:

cd mcp-latex-server

# Install dependencies
uv venv .venv
uv pip install -r requirements.txt

# Start MCP Server
uv run latex_server.py

4. Configure MCP Server in Gemini CLI

Add the LaTeX MCP server to your Gemini CLI configuration:

1. Open or create the Gemini settings file:

# Create .gemini directory if it doesn't exist
mkdir -p ~/.gemini

# Edit settings.json (use your preferred editor)
open ~/.gemini/settings.json

2. Add the MCP server configuration:

{
  "mcpServers": {
    "latex": {
      "command": "uv",
      "args": [
        "--directory",
        "<YOUR_PATH>/Roger/mcp-latex-server",
        "run",
        "latex_server.py"
      ],
      "env": {
        "LATEX_BASE_PATH": "<YOUR_PATH>/Roger"
      },
      "cwd": "<YOUR_PATH>/Roger"
    }
  }
}

Important: Replace <YOUR_PATH>/Roger with your actual project path. You can get the full path by running:

cd /path/to/Roger
pwd

5. Populate Your Context (Crucial Step)

The quality of the output depends entirely on the quality of your input.

• Open the context/ folder
• Fill experience.md, projects.md, skills_inventory.md, and other relevant files with your actual data following the examples format that has been provided in the files.

Tip: Be verbose in these master files. Include every metric, every tool used, and every achievement. The agent acts as an editor; it's easier for it to summarize a long list than to invent details that aren't there. For example, instead of "Improved performance," write "Reduced API latency by 40% (200ms to 120ms) by implementing Redis caching."

QuickStart

Step 1: Launch Gemini CLI

The MCP server will start automatically when you launch Gemini CLI:

gemini

Tips: Make sure to enable the Skills feature in Gemini CLI settings. You can do this by going to Settings > Features and enabling "Skills" to allow the agent to use its specialized skills (job-analyzer, experience-selector, resume-generator, etc.).

You should see a message indicating the MCP server is connected and ready.

Step 2: Interactive Workflow

Follow this conversational flow to tailor your application:

Phase 1: Job Analysis

Paste the Job Description (JD) to let the agent understand the role deeply.

User: "I am applying for a Senior Backend Engineer role at Netflix. Here is the JD: [PASTE JD TEXT HERE]. Please analyze it for key requirements and engineering culture."

Tip: If your JD is very long, you can use the built-in text editor: Press Ctrl+A, then enter vim/vi editing mode. Press i (insert mode) to start editing and paste your text. When finished, press Esc, then type :wq to save and exit.

Agent: (Uses job-analyzer to parse the text. It will return a summary extracting keywords like 'Microservices', 'High Concurrency', 'Java', and cultural notes like 'Freedom and Responsibility'.)

Phase 2: Strategy & Selection

Ask the agent to bridge your history with the JD requirements identified in Phase 1.

User: "Based on that analysis, select my top 3 most relevant experiences and projects from the context. Explain why you chose them."

Agent: (Uses experience-selector to read your context/*.md files. It will propose a specific list of bullet points and projects that prove you have the skills Netflix is looking for. Review this output—you can ask the agent to swap a project if you disagree.)

Phase 3: Generation

Command the agent to build the final asset.

User: "Looks good. Generate a strictly 1-page resume using the Jake resume template. Ensure all metrics are highlighted."

Agent: (Uses resume-generator to execute the following pipeline:)

1. Reads the Jake_resume.tex template
2. Injects your selected experience into the placeholders
3. Escapes special characters (like & or %) to prevent errors
4. Connects to the MCP server to write the .tex file and compile it

Output: ✅ Resume generated successfully: /Users/you/Roger/AI_Resume/[timestamp]/resume.pdf

Concept

The Analogy

Your Career Agent does exactly what Roger does:

1. The Closet (Context): Roger has a massive attic filled with costumes and props. Similarly, your context/ folder contains every project, skill, and experience you've ever had. It is the raw material.
2. The Disguise (Skills): Roger looks at a situation and decides, "I need to be a doctor today." Your job-analyzer and experience-selector look at a Job Description and decide, "I need to be a Distributed Systems Expert today."
3. The Persona (Output): Roger steps out as "Dr. Penguin." Your agent generates a PDF that is that specific persona, tailored perfectly to the audience, ignoring all unrelated history.

Core Concept

Roger operates on a philosophy of "Infinite Personas, One Ground Truth."

Most people send the same generic resume to every company. Roger believes that every job application requires a unique character construction.

Architecture

• The Brain (Gemini Agent): The strategist. It decides which persona is needed based on the Job Description. It asks: "Does this role need the Startup Hustler or the Corporate Leader?"
• The Memory (Master Context): The unchangeable truth. Roger never lies about his core data (he really did those things), but he selectively highlights specific memories to build the current narrative.
• The Hands (MCP Server): The execution layer. It stitches the costume (LaTeX Template) together to ensure the presentation is flawless.

Mission

To transform the job application process from a "Copy-Paste" chore into a "Strategic Persona Generation" workflow.

With Roger, you are not just an applicant; you are exactly who they are looking for.

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Features

• Context-Aware Analysis: Goes beyond simple keyword matching. The job-analyzer skill deconstructs Job Descriptions (JDs) to identify hidden requirements, infer the company’s engineering culture, and pinpoint the specific problems they are hiring you to solve.

• Evidence-Based Selection: Instead of rewriting your history from scratch, the experience-selector intelligently mines your context/ folder. It selects the most relevant bullet points, projects, and metrics that align with the JD, ensuring your resume speaks directly to the recruiter’s needs.

• Local Privacy: Your career history contains sensitive PII (Personally Identifiable Information). Roger runs entirely on your local machine—your data never leaves your environment unless you choose to share the final PDF.

• LaTeX Precision: Generates strictly formatted PDFs using a local MCP (Model Context Protocol) server. By leveraging LaTeX's typesetting power, the agent guarantees perfect margins, consistent fonts, and a layout that never breaks, solving the "formatting headaches" typical of Word or Google Docs.

• Self-Correcting Workflow: The agent is resilient. If the LaTeX compilation fails due to a syntax error (like an unescaped special character), the resume-generator skill reads the compiler’s error log, identifies the issue, fixes the .tex source code, and recompiles automatically without user intervention.

Project Structure

Understanding the layout helps you navigate the "Brain" (Gemini) and the "Hands" (MCP Server) of the agent:

Roger/
├── .gemini/                                 # Gemini CLI configuration & skills definitions
│   └── skills/
│       ├── cover-letter-writer/               # Skill: Drafts persuasive, narrative-driven cover letters
│       ├── experience-selector/              # Skill: Filters your master history to find the best matching evidence
│       ├── job-analyzer/                     # Skill: Extracts keywords, tech stack, and cultural cues from JDs
│       └── resume-generator/                 # Skill: Orchestrates file creation, LaTeX escaping, and compilation
│           ├── SKILL.md                     # The skill instruction file
│           └── templates/                   # LaTeX source templates for document generation
│               ├── basic_resume.tex         # Alternative minimalist template
│               ├── default_resume.tex       # The standard professional template (US Letter)
│               └── Jake's_resume.tex        # Example custom template
│
├── context/                                 # [USER DATA] Your Master Record - The "Single Source of Truth"
│   ├── awards.md                            # Honors, awards, and recognitions
│   ├── certificates.md                      # Professional certifications and licenses
│   ├── education.md                         # Academic background and degrees
│   ├── experience.md                        # Your complete, detailed work history with metrics
│   ├── information.md                       # Your personal information (name, contact, location, etc.)
│   ├── projects.md                          # Side projects, open source contributions, and hackathons
│   ├── publications.md                      # Research papers, articles, and publications
│   └── skills_inventory.md                  # A categorized list of your technical stack and languages
│
├── mcp-latex-server/                        # Local server responsible for File I/O & PDF Compilation
│   ├── latex_server.py                      # The Python script running the MCP tools
│   ├── mcp_config.json                      # MCP server configuration file
│   ├── quick_setup.py                       # Quick setup script for the server
│   ├── README.md                            # Server-specific documentation
│   ├── requirements.txt                     # Python dependencies for the server
│   └── TEST_PDF_COMPILATION.md              # Testing guide for PDF compilation
│
├── GEMINI.md                                # Global system context that teaches the Agent about the project structure
└── README.md                                # This file - Project documentation

Customization

Editing Templates

The LaTeX templates are located at .gemini/skills/resume-generator/templates/. You have full freedom to modify the visual design:

• Choose from default_resume.tex, basic_resume.tex, or create your own custom template.

• Change fonts, colors, or section headers using standard LaTeX syntax.

• Important: Keep the Python-style placeholders (e.g., \VAR{experience_section}, \VAR{education_section}) intact. The agent uses these specific markers to know exactly where to inject the generated content.

Updating Your Data

You never need to edit the Agent's prompt or "retrain" it to update your job history. Simply edit the files in the context/ folder directly.

• Added a new certification? Update certificates.md.

• Finished a big project at work? Add a new bullet point to experience.md.

• Received an award? Add it to awards.md.

• The next time you run the agent, it will automatically access the latest data from your context folder.

Troubleshooting

Q: The MCP server isn't connecting. A: Verify your ~/.gemini/settings.json configuration:

• Check that the path to your project directory is correct (use absolute path)
• Ensure uv is installed and in your PATH
• Verify the file exists: ls ~/.gemini/settings.json
• Check the MCP server logs in the Gemini CLI output for error messages

Q: The PDF compilation failed with an error. A: This usually happens if a special character (like &, $, or %) wasn't escaped properly. Tell the Agent: "The compilation failed. Read the error log and fix the syntax." The resume-generator skill has built-in self-correction logic to read the pdflatex log, find the line number of the error, and fix the unescaped character automatically.

Q: The Agent isn't finding my files or context. A: Ensure the LATEX_BASE_PATH in your ~/.gemini/settings.json points to the root of the Roger directory. The agent relies on the relative paths defined in GEMINI.md (e.g., ./context/) to orient itself.

Q: LaTeX command not found. A: Make sure LaTeX is installed and in your PATH. Run pdflatex --version to verify. If not found, add the LaTeX binary directory to your PATH:

• macOS: Add to ~/.zshrc: export PATH="/Library/TeX/texbin:$PATH"
• Linux: Usually installed to /usr/bin/ automatically
• Windows: Add the MiKTeX or TeX Live bin directory to your system PATH environment variable