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

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You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks the agent to find all files in the monorepo that import the @company/auth package to understand how authentication is used across services. Which built-in tool is most appropriate for this task?

• A. Grep, to search for the import statement pattern across file contents
• B. Glob, to find files with "auth" in their filename or path
• C. Bash, to execute find. -type d -name "*auth*" and explore matching directories
• D. Read, starting with package.json files to trace dependency declarations

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Your content curation agent discovers articles, analyzes each for relevance, then adds selected articles to themed collections. With separate discover_articles(topic), analyze_article(id), and add_to_collection(article_id, collection_id) tools, you observe 18+ sequential tool calls per request, causing latency issues. The agent must make editorial judgments about which articles fit a collection's theme - this requires seeing all candidates with their analysis scores simultaneously to select a cohesive set. What tool composition best addresses efficiency while preserving editorial judgment?

• A. Create a curate_collection(topic, collection_id) tool that handles discovery, analysis, and selection internally using configurable quality thresholds.
• B. Create a discover_and_analyze(topic) composite tool that returns all candidates with their analysis scores, keeping add_to_collection separate for selective calls.
• C. Keep all tools separate but implement response caching for analyze_article calls.
• D. Add a preview_curation(topic, collection_id) tool that shows what would be added based on predefined rules, with an approve_curation() tool to confirm.

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Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?

• A. Return is_error: true with the original message content echoed back.
• B. Return is_error: true with a message communicating uncertainty: "Timeout - status unknown.
  Message may have been sent. Avoid retry."
• C. Return is_error: true with a structured field retry_safe: true for timeouts, distinguishing them from permanent failures that should not be retried.
• D. Return is_error: true with a message encouraging retry: "Delivery service temporarily unavailable.
  Please retry the notification."

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After investigating a billing dispute over 25+ turns, you've identified that duplicate charges occurred due to a payment gateway timeout triggering retry logic. The required refund ($847) exceeds your $500 authorization limit You need to call escalate_to_human, and the human agent won't have access to your conversation transcript. What context should you pass to enable effective resolution?

• A. The customer's original complaint verbatim plus the tool result excerpts showing duplicate transactions.
• B. Your diagnosis and the refund amount only.
• C. The complete conversation transcript with all tool results.
• D. A structured summary: customer ID, root cause, refund amount, and recommended action.

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