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 tasked with adding real-time updates to the application. This could be implemented using WebSockets, Server-Sent Events, or polling, each with different complexity, browser support, and infrastructure requirements.
What's the most effective way to begin this task?

• A. Use direct execution to implement polling first, then evaluate whether to upgrade to WebSockets later.
• B. Enter plan mode to explore the architecture, evaluate trade-offs, and present options for team approval before implementing.
• C. Use direct execution with a prompt asking Claude to analyze all approaches and implement the one it determines is best.
• D. Start direct execution with WebSockets, then refactor if infrastructure issues arise.

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.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match - the file has repetitive docstrings, variable names, and structural patterns.
What's the most reliable way to complete this insertion?

• A. Use Bash to append the function definition to the end of the file using heredoc syntax
• B. Use Read to load the file, add the function at the appropriate location, then Write the updated file
• C. Use Edit with an extremely long old_string capturing 30+ lines of context to guarantee uniqueness
• D. Use Edit's replace_all parameter to target a common pattern and embed the new function in the replacement text

You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings. Each finding is a JSON object containing file_path, line_number, issue_category--such as security or style--and description. Developers can dismiss findings they consider unhelpful, and currently 35% of findings are dismissed. You want to analyze these dismissals to understand what the system is getting wrong and improve the prompts accordingly. What change to the output structure would best support this analysis?

• A. Expand the description field with more detailed explanations of why each issue matters and how it should be fixed.
• B. Add a model_confidence field from 0.0 to 1.0 and filter findings below a threshold calibrated against historical dismissal rates.
• C. Add a detected_pattern field recording the specific code construct that triggered the finding, such as single-letter loop variable.
• D. Remove the issue_category field and track dismissal rates only at the individual-finding level.

Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns "industry analysts estimate $50B market size (methodology varies)" and the document analysis agent returns "peer-reviewed study estimates $35B (±$7B, 95% CI)," the coordinator either picks one arbitrarily or produces vague statements like "the market may be
$35B-$50B depending on factors." What systematic approach best addresses this?

• A. Configure subagents to only report findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.
• B. Instruct the synthesis agent to structure reports with explicit sections distinguishing well- established findings from contested ones, preserving original source characterizations and methodological context.
• C. Implement a confidence calibration layer that normalizes subagent uncertainty expressions to standardized probability scores (0.0-1.0), then weight-average findings by their calibrated confidence.
• D. Add a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.