I gave Claude Code our entire codebase. Our customers noticed. | Al Chen (Galileo)
Episode
45 min
Read time
2 min
Topics
Remote Work, Leadership, Artificial Intelligence
AI-Generated Summary
Key Takeaways
- ✓Multi-repo querying setup: Pull all repositories into a single parent directory in VS Code, then run Claude Code at that root level so it can traverse across all repos simultaneously. Use a Claude Code-generated 16-line shell script called "pull all" to sync the latest main branch across all 15 repos daily, replacing manual git pull commands one repo at a time.
- ✓Customer quirks documentation: Maintain a dedicated Confluence page per enterprise customer listing their specific infrastructure constraints — secret management tools, namespace conventions, sidecar configurations, service-to-service encryption requirements. Feed this page into a Claude Code custom command so every deployment answer is automatically tailored to that customer's environment rather than returning generic Kubernetes documentation.
- ✓Custom Claude Code commands: Build slash-command shortcuts (e.g., "/dpl") that chain multiple context sources — Confluence via MCP, Slack threads via MCP, and the local codebase — into a single query. This lets one command pull deployment documentation, customer quirks, and live code simultaneously, producing answers that reflect current architecture rather than potentially outdated public docs.
- ✓Chaos tolerance in documentation: Stop enforcing a single source-of-truth system across teams. Distribute knowledge across Confluence, Notion, and Slack freely, then use Claude Code with MCP connectors to retrieve and synthesize it on demand. The AI navigates fragmented information more effectively than humans curate it, eliminating the overhead of maintaining rigid documentation hierarchies.
- ✓Virtuous customer support loop: When a Slack customer thread produces a useful answer, use tools like Pylon to auto-generate a knowledge base article from that conversation. This converts individual support interactions into searchable, publishable documentation without requiring a formal PR process, creating compounding value from each resolved customer question across the entire customer base.
What It Covers
Al Chen, field engineer at Galileo, demonstrates how non-engineers can clone all 15 of a company's repositories into VS Code, then use Claude Code to answer nuanced enterprise customer deployment questions that public documentation cannot address, while building customer-specific context libraries in Confluence to personalize technical responses.
Key Questions Answered
- •Multi-repo querying setup: Pull all repositories into a single parent directory in VS Code, then run Claude Code at that root level so it can traverse across all repos simultaneously. Use a Claude Code-generated 16-line shell script called "pull all" to sync the latest main branch across all 15 repos daily, replacing manual git pull commands one repo at a time.
- •Customer quirks documentation: Maintain a dedicated Confluence page per enterprise customer listing their specific infrastructure constraints — secret management tools, namespace conventions, sidecar configurations, service-to-service encryption requirements. Feed this page into a Claude Code custom command so every deployment answer is automatically tailored to that customer's environment rather than returning generic Kubernetes documentation.
- •Custom Claude Code commands: Build slash-command shortcuts (e.g., "/dpl") that chain multiple context sources — Confluence via MCP, Slack threads via MCP, and the local codebase — into a single query. This lets one command pull deployment documentation, customer quirks, and live code simultaneously, producing answers that reflect current architecture rather than potentially outdated public docs.
- •Chaos tolerance in documentation: Stop enforcing a single source-of-truth system across teams. Distribute knowledge across Confluence, Notion, and Slack freely, then use Claude Code with MCP connectors to retrieve and synthesize it on demand. The AI navigates fragmented information more effectively than humans curate it, eliminating the overhead of maintaining rigid documentation hierarchies.
- •Virtuous customer support loop: When a Slack customer thread produces a useful answer, use tools like Pylon to auto-generate a knowledge base article from that conversation. This converts individual support interactions into searchable, publishable documentation without requiring a formal PR process, creating compounding value from each resolved customer question across the entire customer base.
Notable Moment
Al Chen described reaching a breaking point when he realized he was manually running git pull on 15 separate repositories one by one. He asked Claude Code to solve the problem, and it produced a working automation script in a single attempt — something he had never written himself.
Episode Transcript
The minute I realized I couldn't really do my job was when I was trying to reference our public documentation and trying to provide an answer. It just still wasn't coming up with the answer that my customers were looking for. They don't want the docs answer. They want the step by step answer of how all these services cascade together. What I realized is that I can actually pull all of these repos into my Versus Code, and I can now use Cloud Code to ask our entire code base questions. Did you just say Claude Code write me a script that pulls all these? Yeah. Yeah. I'm opening up the script right now. It's like, what, 16 lines? Didn't have to write this. I just said, help me figure out a way to pull the latest main branches into my local repos. The reality is we can now all live in a little bit more chaos because the AI navigates all that information for us across systems. Right? So you can be in your code, querying Confluence. It will find the information. You have to be less precious about where and how you store the information. Throw into Confluence, throw into Notion, throw it into Slack, whatever. That ends up being context you can provide to Claude when you are trying to ask it a question about the customer or about your code base. Let's give Claude Code a little spiff every time it answers a question correctly. You gotta split your quota with Claude Code. Yeah. It gives you better answers the more bucks you give it or something. Point operated Claude. That's gonna be my new skill. Welcome back to How IAI. I'm Claire Vaux, product leader and AI obsessive here on a mission to help you build better with these new tools. Today, we have an episode all about harnessing your code to make your customer's experience way better. Al Chen, who's on the field engineering team at Galileo, shows us how he uses their 15 repositories and Claude Code to answer every Nuance customer question that comes across his desk and use that to make the entire customer base and his entire team a lot happier. Let's get to it. This episode is brought to you by Orcus, the company behind Open Source Conductor, which powers complex workflows and process orchestration for modern enterprise apps and agentic workflows. Legacy business process automation tools are breaking down. Siloed low code platforms, outdated process management systems, and disconnected API management tools weren't built for today's AI powered world. Orcus changes that. With Orcus Conductor, you get a modern orchestration layer that scales with high reliability and brings humans, AI, and systems together in real time. It's not just about tasks. It's about orchestrating everything. APIs, microservices, data pipelines, human in the loop actions, and even autonomous agents. So build, test, and debug complex workflows with ease, all while maintaining enterprise grade security, compliance, and observability. Orcus. …
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Books, tools, and gear mentioned in this episode
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Tools
“When a Slack customer thread produces a useful answer, use tools like Pylon to auto-generate a knowledge base article from that conversation.”
by Microsoft
“Al Chen, field engineer at Galileo, demonstrates how non-engineers can clone all 15 of a company's repositories into VS Code, then use Claude Code to answer nuanced enterprise customer deployment questions.”
by Atlassian
“building customer-specific context libraries in Confluence to personalize technical responses.”
by Notion Labs
“Distribute knowledge across Confluence, Notion, and Slack freely, then use Claude Code with MCP connectors to retrieve and synthesize it on demand.”
by Slack Technologies
“Build slash-command shortcuts (e.g., "/dpl") that chain multiple context sources — Confluence via MCP, Slack threads via MCP, and the local codebase — into a single query.”
by Anthropic
“then use Claude Code to answer nuanced enterprise customer deployment questions that public documentation cannot address, while building customer-specific context libraries in Confluence to personalize technical responses.”
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