10 Claude Code Secrets from the Team That Built It
Anthropic's internal playbook - and what PMs can steal from it
Boris Cherny (founder of Claude Code) dropped something rare on X this week: how his own team actually uses the tool. Not marketing. Not a tutorial. The real playbook from engineers who live inside Claude Code 8+ hours a day
Most of us use Claude Code like fancy autocomplete. The Anthropic team? They run it like an engineering org - parallel workstreams, dedicated review processes, institutional memory that compounds over time.
Why does this matter for PMs? Whether you’re building side projects, prototyping features, or simply want to understand how AI-native engineering actually works - this is the playbook. And frankly, the patterns here (planning before execution, outcome-based delegation, compounding documentation) should feel familiar. PMs have been doing this with humans for years.
Here’s how to do it with AI.
The Philosophy First
Three mental models run through everything Boris shared:
Parallelization over serialization. Claude isn’t one assistant. It’s a team you assemble and orchestrate in parallel.
Invest in infrastructure. Your CLAUDE.md and custom skills are onboarding docs - for an AI that reads them perfectly every time.
Delegate outcomes, not steps. Say “fix the failing CI tests” not “open file X, change line Y.”
Build Your AI Infrastructure
Run 3-5 Git Worktrees in Parallel
The team’s biggest force multiplier. Spin up multiple worktrees, each running its own Claude instance. While Claude #1 refactors, Claude #2 writes tests, Claude #3 fixes a bug. You’re not waiting. You’re orchestrating.
Some set up shell aliases (za, zb, zc) to hop between worktrees in one keystroke.
Invest in Your CLAUDE.md
After every correction, say: “Update your CLAUDE.md so you don’t make that mistake again.”
Claude is remarkably effective at codifying its own rules. One engineer has Claude maintain a notes directory for every project, updated after every PR. The CLAUDE.md points to these notes. Institutional memory, built automatically.
Create Custom Skills, Commit to Git
If you do something more than once a day, turn it into a skill. Team examples:
/techdebtcommand to find and kill duplicated code at session endSlash command syncing 7 days of Slack, GDrive, Asana, GitHub into one context dump
Analytics agents that write dbt models and test changes in dev
These live in git. Reusable across projects.
Optimize Your Terminal
The team loves Ghostty. They customize status bars to show context usage and git branch. Many use tmux with color-coded tabs - one per worktree.
Unexpected tip: use voice dictation. You speak 3x faster than you type, and prompts get way more detailed. On macOS, hit fn twice.
`The PM angle: Think of this as building the “operating system” for your AI workflow. Upfront investment, compounding returns - just like setting up good team processes.
The Planning Mindset
Start Complex Tasks in Plan Mode
Pour energy into the plan so Claude can one-shot the implementation.
One person has Claude write the plan, then spins up a second Claude to review it as a staff engineer. Built-in code review before any code is written.
Rule: the moment something goes sideways, switch back to plan mode. Don’t keep pushing.
Level Up Your Prompting
Challenge Claude. “Grill me on these changes and don’t make a PR until I pass your test.”
Demand elegance. After a mediocre fix: “Knowing everything you know now, scrap this and implement the elegant solution.”
Reduce ambiguity. Write detailed specs before handing work off. More specific = better output. Every time.
The PM angle: This is where your skills directly translate. You’re essentially writing PRDs - for an AI. Ambiguity in, garbage out.
Execution & Debugging
Let Claude Fix Bugs By Itself
Enable Slack MCP, paste a bug thread, say “fix.” Zero context switching.
Or simply: “Go fix the failing CI tests.” Don’t explain how.
Point Claude at docker logs for distributed systems debugging - more capable than you’d expect.
Use Subagents
Append “use subagents” when you want Claude to throw more compute at a problem. Keeps your main agent’s context window clean.
Example: “use 5 subagents to explore the codebase” - Claude spins up parallel agents for entry points, React components, state management, testing infrastructure.
Use Claude for Data & Analytics
Use the “bq” CLI to pull and analyze metrics on the fly. The team has a BigQuery skill in their codebase.
Boris’s claim: hasn’t touched SQL in over six months.
Works for any database with a CLI, MCP, or API.
The PM angle: The “don’t micromanage” philosophy. Give Claude the problem, not the solution. Same way you’d delegate to a strong engineer.
Learning & Growing
Use Claude Code to Learn
Enable “Explanatory” output style in
/configto understand the why behind changes.Generate HTML presentations explaining unfamiliar code. The output quality is better than you’d think.
Ask for ASCII diagrams of protocols and codebases.
Build a spaced-repetition skill: explain your understanding, Claude asks follow-ups to fill gaps, stores the result.
The PM angle: Use Claude Code not just to build, but to learn. Perfect for PMs ramping up on technical domains or understanding a new codebase.
Why This Matters for PMs
Building side projects? This is your playbook for real velocity.
Managing eng teams? Understanding these workflows helps you have better conversations about AI tooling adoption.
Thinking about AI strategy? “Skills as git commits” is how institutional knowledge gets codified in AI-first orgs. Companies that figure this out first will have compounding advantages.
These aren’t 10 random hacks. They’re a system for human-AI collaboration.