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's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting - sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them. What is the most effective way to ensure all generated code is consistently formatted?

• A. Add a Stop hook with a prompt-based check that evaluates whether generated code follows formatting standards and prompts Claude to fix violations.
• B. Extract the formatting rules into a dedicated skill that Claude loads automatically when generating code, with more detailed examples of correct formatting.
• C. Split the formatting rules into path-scoped .claude/rules/files that load when Claude works on matching file types.
• D. Configure a PostToolUse hook with an Edit|Write matcher that automatically runs Prettier on each file Claude modifies.

After deploying the automated review, you notice high precision but low recall--real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to
"only report high-confidence issues you are certain about" and "err on the side of not commenting." Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?

• A. Add detailed few-shot examples demonstrating bug categories Claude should flag--race conditions, null dereferences, and error-handling gaps--while retaining the high-confidence filtering instruction.
• B. Remove the conservative instructions and have Claude report every potential issue, then apply a programmatic filter that deduplicates findings and suppresses historically noisy categories.
• C. Expand the context to include related tests, recent Git history, and the module's dependency graph so Claude has richer evidence for judging severity.
• D. Split the review into a finding stage whose objective is comprehensive coverage--reporting every potential issue with confidence and severity metadata--and a separate stage that verifies and thresholds those findings.

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 automated review generates many findings per pull request, but developer feedback shows that approximately half are dismissed as "not worth addressing." Analysis reveals that these findings are often technically accurate but involve minor style preferences or patterns that are acceptable in the project.
Before adding infrastructure complexity, what prompt-design change would most effectively reduce dismissals while maintaining detection of genuine issues?

• A. Define explicit reporting criteria that distinguish reportable bugs and security issues from minor style preferences and accepted local patterns.
• B. Ask Claude to rate each finding's confidence from 1 to 10 and include only findings rated 8 or higher.
• C. Add the instruction: "Only report findings you are highly confident are genuine problems."
• D. Add a secondary classification model that filters findings according to predicted developer acceptance.

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