Anthropic CCA-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.
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. 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.
• B. Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to 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. Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.

An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?

• A. Max tokens
• B. Context window
• C. Temperature
• D. Prompt length

A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

• A. Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
• B. Implement sliding window context that retains the most recent 30 turns.
• C. Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.
• D. Summarize earlier turns into a narrative description, preserving full message history only for the active issue.

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

After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

• A. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
• B. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
• C. Pass the full accumulated context from all prior agents.
• D. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.