Context Engineering for Gemini CLI

A comprehensive template for getting started with Context Engineering - the discipline of engineering context for AI coding assistants so they have the information necessary to get the job done end to end.

Context Engineering is 10x better than prompt engineering and 100x better than vibe coding.

Quick Start

# 1. Clone this template
git clone https://github.com/rickscode/Gemini-CLI-Context-Engineer.git
cd Gemini-CLI-Context-Engineer

# 2. Install Gemini CLI (if not already installed)
# For macOS/Linux:
curl -fsSL https://storage.googleapis.com/generative-ai-cli/install.sh | sh

# Or download from: https://github.com/google-gemini/generative-ai-cli/releases

# 3. Create your initial feature request
# Edit INITIAL.md with your feature requirements

# 4. Generate a comprehensive PRP (Product Requirements Prompt)
./scripts/generate-prp.sh INITIAL.md

# 5. Execute the PRP with Gemini CLI to implement your feature
./scripts/execute-prp.sh PRPs/your-feature-name.md

Table of Contents

• What is Context Engineering?
• Template Structure
• Step-by-Step Guide
• Writing Effective INITIAL.md Files
• The PRP Workflow
• Using Examples Effectively
• Best Practices

What is Context Engineering?

Context Engineering represents a paradigm shift from traditional prompt engineering:

Prompt Engineering vs Context Engineering

Prompt Engineering:

• Focuses on clever wording and specific phrasing
• Limited to how you phrase a task
• Like giving someone a sticky note

Context Engineering:

• A complete system for providing comprehensive context
• Includes documentation, examples, rules, patterns, and validation
• Like writing a full screenplay with all the details

Why Context Engineering Matters

1. Reduces AI Failures: Most agent failures aren't model failures - they're context failures
2. Ensures Consistency: AI follows your project patterns and conventions
3. Enables Complex Features: AI can handle multi-step implementations with proper context
4. Self-Correcting: Validation loops allow AI to fix its own mistakes

Template Structure

context-engineering-intro/
├── scripts/
│   ├── generate-prp.sh    # Generates comprehensive PRPs
│   └── execute-prp.sh     # Executes PRPs to implement features
├── PRPs/
│   ├── templates/
│   │   └── prp_base.md       # Base template for PRPs
│   └── EXAMPLE_multi_agent_prp.md  # Example of a complete PRP
├── examples/                  # Your code examples (critical!)
├── INITIAL.md               # Template for feature requests
├── INITIAL_EXAMPLE.md       # Example feature request
└── README.md                # This file

This template doesn't focus on RAG and tools with context engineering because I have a LOT more in store for that soon. ;)

Step-by-Step Guide

1. Authenticate with Gemini CLI

Before using the scripts, authenticate with your Google account:

# Login to get free Gemini Code Assist license
gemini auth login

# Verify authentication
gemini auth status

This gives you access to Gemini 2.5 Pro with 1M token context window and generous rate limits (60 requests/minute, 1000/day).

2. Create Your Initial Feature Request

Edit INITIAL.md to describe what you want to build:

## FEATURE:
[Describe what you want to build - be specific about functionality and requirements]

## EXAMPLES:
[List any example files in the examples/ folder and explain how they should be used]

## DOCUMENTATION:
[Include links to relevant documentation, APIs, or MCP server resources]

## OTHER CONSIDERATIONS:
[Mention any gotchas, specific requirements, or things AI assistants commonly miss]

See INITIAL_EXAMPLE.md for a complete example.

3. Generate the PRP

PRPs (Product Requirements Prompts) are comprehensive implementation blueprints that include:

• Complete context and documentation
• Implementation steps with validation
• Error handling patterns
• Test requirements

They are similar to PRDs (Product Requirements Documents) but are crafted more specifically to instruct an AI coding assistant.

Run the generation script:

./scripts/generate-prp.sh INITIAL.md

This command will:

1. Read your feature request from INITIAL.md
2. Extract the current codebase structure
3. Create a comprehensive PRP in PRPs/your-feature-name.md
4. Include validation commands and implementation guidelines

4. Execute the PRP

Once generated, execute the PRP with Gemini CLI to implement your feature:

./scripts/execute-prp.sh PRPs/your-feature-name.md

The script will:

1. Pass the PRP to Gemini CLI
2. Let Gemini CLI read all context from the PRP
3. Gemini CLI will implement the feature based on the requirements
4. Review the generated files and test the implementation

How It Works

Generate Script (generate-prp.sh)

• Reads your INITIAL.md requirements
• Extracts feature name automatically
• Creates current codebase tree
• Generates Gemini CLI-specific context prompt
• Outputs structured PRP file

Execute Script (execute-prp.sh)

• Validates PRP file exists
• Checks Gemini CLI availability
• Passes PRP file to Gemini CLI
• Lets Gemini CLI handle the implementation

Writing Effective INITIAL.md Files

Key Sections Explained

FEATURE: Be specific and comprehensive

• "Build a web scraper"
• "Build an async web scraper using BeautifulSoup that extracts product data from e-commerce sites, handles rate limiting, and stores results in PostgreSQL"

EXAMPLES: Leverage the examples/ folder

• Place relevant code patterns in examples/
• Reference specific files and patterns to follow
• Explain what aspects should be mimicked

DOCUMENTATION: Include all relevant resources

• API documentation URLs
• Library guides
• MCP server documentation
• Database schemas

OTHER CONSIDERATIONS: Capture important details

• Authentication requirements
• Rate limits or quotas
• Common pitfalls
• Performance requirements

The PRP Workflow

How the Scripts Work

generate-prp.sh follows this process:

1. Input Validation

   • Checks if INITIAL.md exists
   • Verifies Gemini CLI is available

2. Feature Extraction

   • Extracts feature name from input file
   • Creates organized PRP structure

3. Context Building

   • Adds current codebase tree
   • Includes implementation guidelines
   • Adds validation steps

4. PRP Generation

   • Creates Gemini CLI-specific prompt
   • Outputs structured markdown file

execute-prp.sh follows this process:

1. Validation: Checks PRP file exists and Gemini CLI is available
2. Execution: Passes PRP to Gemini CLI with gemini -f command
3. Completion: Gemini CLI implements the feature based on the PRP context

See PRPs/recipe-optimizer.md for a complete example of what gets generated.

Using Examples Effectively

The examples/ folder is critical for success. AI coding assistants perform much better when they can see patterns to follow.

What to Include in Examples

1. Code Structure Patterns

   • How you organize modules
   • Import conventions
   • Class/function patterns

2. Testing Patterns

   • Test file structure
   • Mocking approaches
   • Assertion styles

3. Integration Patterns

   • API client implementations
   • Database connections
   • Authentication flows

4. CLI Patterns

   • Argument parsing
   • Output formatting
   • Error handling

Example Structure

examples/
├── README.md           # Explains what each example demonstrates
├── cli.py             # CLI implementation pattern
├── agent/             # Agent architecture patterns
│   ├── agent.py      # Agent creation pattern
│   ├── tools.py      # Tool implementation pattern
│   └── providers.py  # Multi-provider pattern
└── tests/            # Testing patterns
    ├── test_agent.py # Unit test patterns
    └── conftest.py   # Pytest configuration

Best Practices

1. Be Explicit in INITIAL.md

• Don't assume the AI knows your preferences
• Include specific requirements and constraints
• Reference examples liberally

2. Provide Comprehensive Examples

• More examples = better implementations
• Show both what to do AND what not to do
• Include error handling patterns

3. Use Validation Gates

• PRPs include test commands that must pass
• AI will iterate until all validations succeed
• This ensures working code on first try

4. Leverage Documentation

• Include official API docs
• Add MCP server resources
• Reference specific documentation sections

5. Customize GEMINI.md

• Add your conventions
• Include project-specific rules
• Define coding standards

Resources

• Gemini Code Documentation
• Context Engineering Best Practices