You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
A critical bug is affecting production users. Error logs show exceptions in the OrderProcessing module with a clear stack trace pointing to a specific function. You haven't worked with this module before. What's the most effective approach?

• A. Start with direct execution to gather initial information, then switch to plan mode to design a comprehensive solution before implementing any changes.
• B. Enter plan mode to explore the module's architecture and dependencies before attempting any fixes.
• C. Use plan mode to analyze the error in context of the module's design, enumerate potential root causes, and prioritize fixes systematically.
• D. Use direct execution to examine the stack trace, read the relevant code, and implement a fix once you identify the root cause.

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing /migrate-component. The workflow should stay in sync as the team iterates on it. Where should you place the skill file?

• A. In the project's.claude/settings.json using a skillOverrides entry to register and define the workflow
• B. In .claude/skills/migrate-component/SKILL.md at the project root, committed to version control
• C. As a detailed instruction block in the project's root CLAUDE.md file
• D. In ~/.claude/skills/migrate-component/SKILL.md on each developer's machine

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?

• A. Add both servers to your local ~/.claude.json
• B. Add venue server to ~/.claude.json and playlist server to .mcp.json
• C. Add both servers to the project-level .mcp.json file
• D. Add venue server to .mcp.json and playlist server to ~/.claude.json.

Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails - the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like "I'm having trouble with that request" instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

• A. Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.
• B. Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.
• C. Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.
• D. Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.