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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.
You're implementing a new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py, error_handlers.py, and audit_logger.py. This is a one-off integration task - these patterns are well-documented in your team wiki and don't need additional project-level documentation. What's the most effective approach?

• A. Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.
• B. Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow.
• C. Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.
• D. Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.

A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API. What is the primary mechanism your application uses to determine whether to continue the loop or stop?

• A. You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
• B. You check whether Claude's response contains a text content block - if text is present, the agent has produced its final answer and the loop should exit.
• C. You check the stop_reason field in each API response - the loop continues while it equals
  "tool_use" and exits when it changes to "end_turn" or another terminal value.
• D. You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.

Your test-generation process produces unit tests for new code, but reviews show that 55% are low-value: trivial assertions that verify only that functions do not throw exceptions, tests that duplicate existing coverage, or tests that ignore your team's fixture conventions. How should you reduce the rate of low-value tests being generated in the first place?

• A. Restrict test generation to directories where historical quality metrics show higher acceptance rates, disabling it in areas where generated tests consistently require substantial editing.
• B. Document your testing standards in CLAUDE.md, including valuable-test criteria, available fixtures and their intended uses, and examples distinguishing meaningful behavioural tests from trivial assertions.
• C. Implement two-phase generation in which a second Claude call scores every test against quality criteria and filters out low-scoring tests before presenting them to developers.
• D. Add post-generation coverage analysis that automatically filters out every generated test that does not increase line coverage beyond the existing test suite.

During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process _refund immediately - but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist. Which change directly addresses the root cause of the agent fabricating the order_id value?

• A. Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
• B. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
• C. Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
• D. Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your test generation produces unit tests for new code, but reviews show that 55% are low-value:
trivial assertions that only verify functions do not throw exceptions, tests duplicating existing coverage, or tests ignoring your team's fixture conventions.
How do you reduce the rate of low-value tests being generated in the first place?

• A. Document testing standards in CLAUDE.md, including valuable-test criteria, available fixtures and their intended use cases, and examples distinguishing meaningful behavioral tests from trivial assertions.
• B. Add post-generation coverage analysis that automatically filters out any generated test that does not increase line coverage beyond existing tests.
• C. Restrict test generation to directories where historical quality metrics show higher acceptance rates, disabling it for areas where generated tests consistently require substantial editing.
• D. Implement two-phase generation in which a second Claude call scores each test against quality criteria, filtering out low-scoring tests before presenting results to developers.