llamero 0.0.1

Tools to facilitate collaborating with LLMs

Development Guidelines

Code Organization for LLM Interaction

When developing this project (or using it as a template), keep in mind these guidelines for effective collaboration with Large Language Models:

1. Separation of Concerns

   • Each package should have a single, clear responsibility
   • New features should be separate packages when appropriate
   • Avoid coupling between packages
   • Use consistent patterns across packages, but implement independently
   • Cross-cutting concerns should use shared conventions

2. File Length and Modularity

   • Keep files short and focused on a single responsibility
   • If you find yourself using comments like "... rest remains the same" or "... etc", the file is too long
   • Files should be completely replaceable in a single LLM interaction
   • Long files should be split into logical components

3. Dependencies

   • All dependencies managed in pyproject.toml
   • Optional dependencies grouped by feature:

     [project.optional-dependencies]
     test = ["pytest", ...]
     site = ["markdown2", ...]
     all = ["pytest", "markdown2", ...]  # Everything
     

   • Use appropriate groups during development:

     pip install -e ".[test]"  # Just testing
     pip install -e ".[all]"   # Everything
     

4. Testing Standards

   • Every new feature needs tests
   • Tests should be clear and focused
   • Use pytest fixtures for common setups
   • All workflows depend on tests passing
   • Test files should follow same modularity principles

5. Why This Matters

   • LLMs work best with clear, focused contexts
   • Complete file contents are better than partial updates with ellipsis
   • Tests provide clear examples of intended behavior
   • Shorter files make it easier for LLMs to:
     • Understand the complete context
     • Suggest accurate modifications
     • Maintain consistency
     • Avoid potential errors from incomplete information

6. Best Practices

   • Aim for files under 200 lines
   • Each file should have a single, clear purpose
   • Use directory structure to organize related components
   • Prefer many small files over few large files
   • Consider splitting when files require partial updates
   • Write tests alongside new features
   • Run tests locally before pushing

LLM-Focused Summary System

Overview

The project includes an automated summary generation system designed to help LLMs efficiently work with the codebase. This system generates both local directory summaries and project-wide summaries to provide focused, relevant context for different tasks.

Types of Summaries

Directory Summaries

Each directory in the project contains a SUMMARY file that concatenates all text files in that directory. This provides focused, local context when working on directory-specific tasks.

Project-Wide Summaries

Special project-wide summaries are maintained in the SUMMARIES/ directory on the summaries branch:

• READMEs.md: Concatenation of all README files in the project
• README_SUBs.md: Same as above but excluding the root README
• PYTHON.md: Structured view of all Python code including:
  • Function and class signatures
  • Type hints
  • Docstrings
  • Clear indication of class membership

Accessing Summaries

Directory Summaries

These are available on any branch in their respective directories:

# Example: View summary for the readme_generator package
cat src/readme_generator/SUMMARY

Project-Wide Summaries

These live exclusively on the summaries branch:

# Switch to summaries branch
git checkout summaries

# View available summaries
ls SUMMARIES/

Using Summaries Effectively

For Local Development

Directory summaries are useful when:

• Getting up to speed on a specific package
• Understanding local code context
• Planning modifications to a package

For Project-Wide Understanding

The SUMMARIES/ directory helps with:

• Understanding overall project structure
• Finding relevant code across packages
• Reviewing API signatures and documentation
• Planning cross-package changes

For LLM Interactions

• Point LLMs to specific summaries based on the task
• Use directory summaries for focused work
• Use project-wide summaries for architectural decisions
• Combine different summaries as needed for context

Implementation Notes

• Summaries are automatically updated on every push to main
• The summaries branch is workflow-owned and force-pushed on updates
• Summary generation is configured in pyproject.toml under [tool.summary]
• Don't modify summaries directly - they're automatically generated

Key Features

• Modular documentation system with Jinja2 templates
• Automatic project structure documentation
• Reusable GitHub Actions workflows
• Centralized configuration management
• Utility functions for common operations
• Clean, maintainable architecture optimized for AI agents
• Git operations handled through utilities