What I Built
I created a Monorepo Deep Audit prompt using Runner H, designed to reverse-engineer large, undocumented codebases — the kind that engineering teams often inherit without context. Through this prompt it acts like a veteran principal engineer reviewing a legacy monorepo from scratch and producing a highly structured, in-depth technical audit.
This workflow solves a real pain point for teams maintaining or onboarding into complex repositories without documentation. It reconstructs system architecture, highlights code smells, identifies deeply coupled modules, and delivers a comprehensive engineering continuity report — all from a single prompt.
Demo
You can see the workflow in action in these screenshots:
- The prompt I gave to Runner H
- The repo I passed as input
- The long-form audit output Runner H generated, section by section
How I Used Runner H
The entire automation runs inside Runner H from a single prompt. No external tools or API integrations were needed. The Runner H agent clones the given GitHub repo, analyzes the structure and contents, and returns a detailed engineering audit in narrative form.
Here’s the exact prompt I used to generate the result:
🧠 The Prompt
You are a senior principal engineer who has been asked to audit and reverse-engineer a legacy monorepo hosted on GitHub at this URL:
<enter your github repo link>
The repository has no documentation and contains multiple packages or folders. Your job is to deeply analyze the entire codebase and generate a comprehensive technical report that is suitable for use by engineering leadership.
Please write the report in plain professional English. Do not use bullet points, markdown symbols, emojis, or code formatting. Write in full paragraphs as if you were submitting this audit to a CTO or senior engineering leadership team.
Your report must be long-form, detailed, and written with clarity and depth. The tone should be expert, confident, and objective. Your goal is to produce a document that can serve as both:
1. A continuity artifact for new engineers joining the project
2. A diagnostic document for leadership to understand architectural and code quality risks
Your report must include:
- A title section with repo name, audit date, and author
- A high-level architecture and code summary
- A detailed breakdown of each major module/folder in 3–4 paragraphs each
- Analysis of code quality, test coverage, typing, modularity, and dependencies
- Description of any repeated logic, circular dependencies, or anti-patterns
- Clear written recommendations for improving the architecture and maintainability
- A final summary on how to onboard new engineers based on your findings
The response must be at least 4000–5000 words.
Again: write in narrative paragraph form only. No bullet points, no markdown, no emojis. Make it read like a printable audit document.
Use Case & Impact
This audit prompt replaces weeks of engineering effort with a well-structured, automated technical assessment. Instead of spending days understanding undocumented systems, teams can get a full architectural overview, system health indicators, and specific improvement suggestions — all in minutes.
Who Benefits?
- Engineering leads inheriting old codebases
- CTOs seeking architectural clarity before a refactor
- Open-source maintainers looking to improve their repo's maintainability
- New engineers joining a large team
By keeping it self-contained and prompt-only, this agent makes it easy to replicate, customize, and adapt to any codebase without setup or integration work.
If you try this workflow or improve upon the prompt, I’d love to see your take on it — feel free to remix it and tag me!
Final Thoughts
This project demonstrates how far agent prompting has come. Runner H handled the entire reasoning loop: inspecting structure, identifying code smells, and recommending concrete refactors — all from a single, well-designed prompt.
It's a practical example of AI stepping into real engineering workflows to handle tasks that used to require deep experience and hours of effort.
Prompt engineering is no longer about fancy syntax — it’s about giving AI real responsibilities.
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