Anthropic CCAR-F Value Pack (Frequently Bought Together)

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 has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response) in addition to tools. How do these MCP prompts become accessible within Claude Code?

• A. They appear as slash commands (e.g., mcp_servername_deploy_checklist) that you can invoke, with arguments passed after the command name.
• B. They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task.
• C. They are surfaced as @-mentionable resources alongside files, fetched and attached to your message when referenced.
• D. They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session.

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

After the web search and document analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?

• A. Include the complete findings from both subagents directly in the synthesis subagent's prompt
• B. Provide the subagent with tool definitions that allow it to request outputs from other subagents via callbacks
• C. Pass reference identifiers and configure the subagent with read access to a shared memory store where other subagents deposited their results
• D. Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator

A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate their account. What should the agent do?

• A. Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating.
• B. First call get_customer and lookup_order to gather account context, then escalate to a human agent.
• C. Immediately call escalate_to_human with the conversation history.
• D. Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats their request.

The synthesis agent completes its initial pass but flags that three key research questions remain unanswered because the web search and document analysis agents didn't find relevant information on those specific subtopics. The coordinator currently proceeds directly to report generation, producing reports with incomplete coverage. What change would most effectively improve research completeness?

• A. Give the synthesis agent direct access to web search tools so it can autonomously fill knowledge gaps without returning control to the coordinator.
• B. Have the coordinator evaluate synthesis output for gaps, then re-delegate to web search and document analysis with targeted queries before invoking synthesis again.
• C. Have the report generation agent note which research questions couldn't be answered, so users understand the limitations of the final output.
• D. Increase the initial breadth of queries sent to web search and document analysis to reduce the probability of missing relevant information.