How I Use Claude Code: Parallel Agents, Slash Commands, and Verification Hooks

Boris's setup treats Claude Code as an extra pair of hands, running five local Claude instances and another five to ten in the browser, all coordinated through system notifications and simple tab management. He starts sessions from his terminal, Chrome, and even his iOS app, switching between them as the work demands, and relies on the Opus 4.5 model for its superior tool use despite its size. The core insight is that you don't need a custom workflow-just parallel agents, plan mode, and a solid verification loop to get high-quality code quickly. The team shares a single CLAUDE.md file in the repository, committing lessons whenever Claude makes a mistake so the model learns from the collective experience. During pull-request reviews, they tag @.claude to inject AI-generated suggestions directly into the PR via a dedicated GitHub action, turning the model into a collaborative reviewer. Every recurring task-commits, pushes, PR creation-has a slash command stored in .claude/commands, eliminating repetitive prompts and letting Claude execute the workflow with minimal back-and-forth. Subagents like code-simplifier and verify-app encapsulate common post-processing steps, while a PostToolUse hook guarantees code is properly formatted before CI runs. Permissions are managed proactively through /permissions entries in .claude/settings.json, avoiding disruptive prompts during execution. Finally, Boris emphasizes a feedback loop: each change Claude generates is automatically tested via the Claude Chrome extension or custom agents, ensuring the AI's output is validated in the real environment. This loop multiplies output quality and gives teams confidence to scale AI assistance across codebases. For technical leaders, the take-away is clear: a disciplined, shared configuration and rigorous verification turn a powerful LLM into a dependable, scalable teammate, boosting productivity while keeping technical debt in check.