egokit 1.0.2

Policy Engine & Scaffolding for AI coding agents

EgoKit

EgoKit is a stateless, file-based compiler that transforms organizational policies into AGENTS.md content and AI-native slash command prompts. EgoKit generates the rules; modern AI coding tools interpret and execute them.

Table of Contents

• Overview
• Installation
• Quick Start
• Command Reference
• Generated Artifacts
• Further Reading

Overview

EgoKit addresses a fundamental challenge with AI coding assistants: they forget organizational standards between sessions and sometimes within the same conversation. Teams repeatedly re-explain the same requirements while each developer's AI produces code that drifts from established patterns.

EgoKit solves this by compiling your organization's policies into artifacts that AI coding tools consume natively. You define your standards once in a policy registry. EgoKit compiles them into AGENTS.md (the emerging standard for AI agent configuration) and a set of slash commands that reinforce those policies during development sessions.

The compilation model means EgoKit has no runtime component. It reads your policy files, generates output artifacts, and exits. The AI coding tools (Claude Code, Augment, Cursor, and others) read those artifacts and enforce your policies during their normal operation.

Installation

EgoKit requires Python 3.13 or later.

# Using UV (recommended)
uv add egokit

# Or using pip
pip install egokit

For development installation:

git clone https://github.com/brannn/egokit.git
cd egokit
uv sync --dev

Quick Start

Initialize a policy registry in your organization's configuration repository:

ego init --org "Your Organization"

This creates the policy registry structure:

.egokit/policy-registry/
├── charter.yaml          # Policy rules with severity and detectors
├── ego/
│   ├── global.yaml       # Organization-wide AI behavior settings
│   └── teams/            # Team-specific overrides
└── schemas/              # JSON Schema validation files

Define your policies in the charter and ego configuration files, then apply them to any project repository:

ego apply --repo /path/to/project --registry /path/to/policy-registry

After generating the initial registry, use your AI coding assistant to refine the charter rules and ego settings based on your team's specific requirements. You can use your AI assistant to help develop and maintain these files once it is aware of the EgoKit schema in the registry's schemas/ directory.

EgoKit generates AGENTS.md and slash commands in the target repository. AI coding tools read these files automatically.

For detailed configuration examples and advanced usage, see the User Guide.

Command Reference

Command	Description
ego init	Create a new policy registry with starter templates
ego apply	Compile policies into AGENTS.md and slash commands
ego doctor	Display current policy configuration and validation status
ego watch	Monitor registry for changes and recompile automatically

Common Options

The apply command accepts these options:

Option	Description
--repo PATH	Target repository for generated artifacts
--registry PATH	Source policy registry location
--dry-run	Preview generated content without writing files
--force	Overwrite existing AGENTS.md without confirmation

Generated Artifacts

EgoKit produces two categories of output:

AGENTS.md

The primary configuration file that AI coding tools read to understand your organizational policies. AGENTS.md contains policy rules organized by severity, behavioral guidelines, and security considerations. EgoKit manages a fenced section within AGENTS.md, allowing you to maintain custom content before and after the generated policies.

Slash Commands

EgoKit generates eight slash commands in both .claude/commands/ and .augment/commands/ directories:

Command	Purpose
/ego-validate	Check current work against policies defined in AGENTS.md
/ego-rules	Display active policy rules and their severity levels
/ego-checkpoint	Capture compliance state before making changes
/ego-review	Run pre-commit review checklist
/ego-security	Security-focused review of specified file or staged changes
/ego-refresh	Re-read AGENTS.md to prevent policy drift
/ego-stats	Analyze historical violation patterns
/ego-suggest	Propose new rules based on codebase patterns
/ego-persona	Switch working persona (developer, writer, reviewer, architect)

These commands are pure AI prompts that reference AGENTS.md. They contain no CLI invocations and work identically across Claude Code and Augment.

Further Reading

• User Guide - Comprehensive usage examples and configuration reference
• System Architecture - Technical internals for contributors