levelup-init

Scan brownfield codebases to generate Context Directive Records.

Updated Jul 7, 2026

Search Tags:#automation #codebase #analysis #multi-agent #governance #directives

Ready to run

npx skills add https://github.com/eavichay/mycli --skill levelup-init-eavichay

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: levelup-init
Source: https://github.com/eavichay/mycli/tree/main/.cursor/skills/levelup-init
Command: npx skills add https://github.com/eavichay/mycli --skill levelup-init-eavichay

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill solves the challenge of maintaining consistent coding standards and governance across large, multi-sub-system codebases by automating the discovery of patterns and the generation of Context Directive Records (CDRs).

Core Features & Use Cases

Multi-Agent Analysis: Orchestrates specialized agents for discovery, pattern classification, and synthesis to ensure comprehensive codebase coverage.
Cross-Sub-System Detection: Identifies patterns and inconsistencies across different modules, flagging them for team review.
Constitution Generation: Automatically derives governance principles from codebase evidence to bootstrap or amend your team's constitution.

Quick Start

Run the levelup-init skill to scan the current repository and generate context directive records for team-ai-directives.

Frequently Asked Questions about levelup-init

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate codebase analysis for team-ai-directives in a brownfield project?▼

Automate codebase analysis for team-ai-directives by running a multi-agent pipeline that scans brownfield codebases to discover reusable patterns and generate Context Directive Records.

How do I detect cross-sub-system inconsistencies in a monorepo architecture?▼

Detect cross-sub-system inconsistencies in a monorepo architecture using specialized agents that identify patterns and flag inconsistencies across different modules for team review.

Can I automatically generate a governance constitution from existing codebase evidence?▼

You can automatically generate a governance constitution from codebase evidence by synthesizing discovered patterns to bootstrap or amend your team's governance principles.

Does this multi-agent codebase analysis work with multi-sub-system architectures?▼

This multi-agent codebase analysis explicitly applies to monorepo and multi-sub-system architectures, satisfying requirements for state persistence and cross-sub-system inconsistency detection.

What is the best way to standardize coding standards across large codebases?▼

The best way to standardize coding standards across large codebases is automating pattern discovery and Context Directive Record generation to maintain consistent governance.

Why does cross-sub-system pattern discovery require a multi-agent pipeline?▼

Cross-sub-system pattern discovery requires a multi-agent pipeline because orchestrating specialized agents for discovery, classification, and synthesis ensures comprehensive codebase coverage.