Based on the provided code fragment, here's a breakdown of the Claude Code architecture, focusing on prompt design, LLM calling, and related logic:

1. Auto-Updater (eK1 function):

   • The eK1 function checks for updates to the CLI. It fetches the latest version from zz2() and compares it to the current version.
   • If an update is available, it triggers the auto-updater (rs()).
   • Telemetry events (I0) are logged for successful and failed auto-updates.
   • The component displays update status messages to the user.

2. Token Usage Warning (dN1 function):

   • The dN1 function displays a warning message when the token usage is high, prompting the user to run /compact.
   • The token usage threshold is determined by IN1 and uA9, which are based on tK1 (190000).

3. Main Interactive Component (TA9 function):

   • This is the core component for the interactive CLI.

   • It manages the input field, command suggestions, message history, and tool execution.

   • Prompt Design:

     • The component handles both "prompt" and "bash" modes.

     • It supports pasting text from the clipboard (K1 function), which is then included in the prompt.

     • It uses command suggestions (nf2) to help the user construct prompts.

   • LLM Calling:

     • The F1 function sends the user's input to the LLM.

     • It uses an AbortController to allow the user to cancel the request.

     • It calls the Qo function (likely responsible for LLM interaction) to execute the prompt.

     • The Qo function takes commands, tools, verbose, slowAndCapableModel, and maxThinkingTokens as options.

     • The response from the LLM is streamed back to the user.

   • Other Logic:

     • It uses the ink library for terminal UI.

     • It manages the input mode (prompt or bash).

     • It handles command history.

     • It displays auto-update status.

     • It tracks token usage.

     • It handles "exit" and "quit" commands.

4. Utility Functions:

   • sf2: Creates a string "[Pasted text +{number} lines] "
   • OA9: Exits the CLI.
   • tf2: Logs a telemetry event for startup time.
   • Iq2: Checks the status of the API key.
   • dq2: Handles cancelling requests using the escape key.

5. Tool Use Permission Check (mA9 function):

   • The mA9 function checks whether the user has granted permission to use a tool.
   • It uses the UH function (likely for checking configuration) to determine if the tool is already approved.
   • If the tool is not approved, it displays a prompt to the user asking for permission.
   • Telemetry events are logged for granted and rejected tool uses.

6. Message History Management (Wq2 function):

   • The Wq2 function updates the message history in yoga layout.

7. Binary Feedback (Aq2, wq2, Vq2, Xq2 functions):

   • These functions handle binary feedback for LLM responses, allowing users to compare two responses and choose the better one.
   • Telemetry events are logged for feedback decisions.

8. LLM Calling Pipeline (cB function):

   • This function seems to be the core LLM calling pipeline.
   • It takes a list of messages, a system prompt, and a set of tools as input.
   • It calls the bs function (likely for constructing the prompt) and the jA9 function (likely for calling the LLM).
   • It handles tool use by calling the kA9 or xA9 functions.
   • It yields messages to the user as they are received.

9. Tool Execution (Yq2, pA9 functions):

   • The Yq2 function executes a tool.
   • It finds the tool definition based on the tool name.
   • It validates the tool input using the tool's input schema.
   • It calls the tool's call method.
   • The pA9 function handles the actual execution of the tool.
   • It parses the tool input, validates it, and then calls the tool's call method.
   • It yields progress messages and result messages to the user.

10. Tool Input Sanitization (cA9 function):

    • The cA9 function sanitizes the input to certain tools, such as the Bash tool.

11. Error Handling (iA9 function):

    • The iA9 function formats error messages for display to the user.

12. Message Rendering (GN1, nA9 functions):

    • The GN1 function renders a message in the terminal.
    • The nA9 function renders tool-specific messages.

13. Binary Feedback UI (_q2, Dq2 functions):

    • These functions implement the UI for binary feedback, allowing users to compare two LLM responses and choose the better one.

14. Thinking Indicator (nu function):

    • The nu function determines the number of "thinking" tokens to use based on the user's input.

15. Main Application Component (kJ1 function):

    • This is the main application component.
    • It manages the overall state of the CLI, including the message history, input mode, and loading state.
    • It calls the N1 function to send the initial prompt to the LLM.
    • It calls the F1 function to send subsequent prompts to the LLM.
    • It renders the message history and the input field.

16. MCP (Model Context Protocol) Integration:

    • The code includes extensive logic for interacting with MCP servers.
    • The FN1 class implements an MCP client.
    • The gN1 class implements a transport for communicating with MCP servers over stdio.
    • The code includes functions for discovering and connecting to MCP servers.
    • The code includes functions for retrieving prompts and tools from MCP servers.

17. Tool Definitions:

    • The code defines several tools, including Task (_U), Architect (mq2), and StickerRequest (cq2).
    • The Task tool allows the LLM to launch a new task.
    • The Architect tool allows the LLM to analyze technical requirements and produce implementation plans.
    • The StickerRequest tool allows the LLM to request stickers from Anthropic.

18. Configuration:

    • The code uses the q2 function to access configuration settings.
    • The code includes logic for managing API keys and other settings.

19. Telemetry:

    • The code uses the I0 function to log telemetry events.

20. Security:

    • The code includes various security measures, such as permission checks and restrictions on certain commands.

In summary, this code fragment reveals a sophisticated architecture for interacting with LLMs, including dynamic prompt construction, streaming support, tool calling, and extensive context management. The code also includes various features for improving the user experience, such as configuration management, error reporting, and auto-updating. The MCP integration suggests that the CLI can be extended with additional functionality via external servers.

Based on the provided code, here's what I can tell you about the Claude Code CLI program, focusing on aspects relevant to prompt design, LLM calling, and related logic:

System Prompts

The code defines several system prompts used in different parts of the application:

1. uq2 function (Agent tool prompt):

   • This function constructs a system prompt for the Agent tool.

   • The prompt instructs the LLM on how to use the available tools effectively, especially when searching for keywords or files.

   • It emphasizes the importance of launching multiple agents concurrently for performance and provides guidelines on how to interact with the agent.

   • It also specifies that the agent cannot use certain tools like Bash, R8, p7, and RI and that the agent's outputs should generally be trusted.

2. Oq2 variable (Architect tool prompt):

   • This variable defines the system prompt for the Architect tool.

   • The prompt instructs the LLM to act as an expert software architect, analyzing technical requirements and producing clear, actionable implementation plans.

   • It emphasizes the need for specific and detailed plans that can be carried out by a junior software engineer.

   • It also instructs the LLM not to write the code or use any string modification tools and not to ask the user if it should implement the changes.

3. bq2 variable (StickerRequest tool prompt):

   • This variable defines the system prompt for the StickerRequest tool.

   • The prompt instructs the LLM on when to use the tool, specifically when a user expresses interest in receiving Anthropic or Claude stickers, swag, or merchandise.

   • It provides common trigger phrases to watch for and specifies that the tool handles the entire request process by showing an interactive form to collect shipping information.

   • It also includes a NOTE that the tool should only be used if the user has explicitly asked to send or give them stickers.

4. FN1 class (MCP server):

   • This class handles communication with MCP servers.

   • The _oninitialize method returns the server capabilities and instructions to the client.

LLM Calling Method

The code uses different methods for calling the LLM, depending on the context:

1. cB function (main LLM call):

   • This function appears to be the primary method for calling the LLM.

   • It takes a list of messages, a system prompt, a tool list, and other options.

   • It calls the LLM and returns the result text.

   • It also handles tool usage and retries.

2. sq2 function (MCP server):

   • This function creates an MCP server that can be used to call the LLM.

   • The MCP server handles requests for tools and prompts.

   • The MCP server calls the LLM and returns the result text.

3. gN1 class (stdio transport):

   • This class handles communication with MCP servers using stdio transport.

   • It reads messages from stdin and writes messages to stdout.

4. FN1 class (MCP client):

   • This class handles communication with MCP servers.

   • It uses the request method to send requests to the MCP server and receive responses.

   • It uses the notification method to send notifications to the MCP server.

Other Related Logic

1. Tool Definitions:

   • The code defines several tools, including Task (_U), Architect (mq2), and StickerRequest (cq2).

   • Each tool has a description, prompt, inputSchema, call function, and other properties.

   • The prompt property is used to provide a description of the tool to the LLM.

   • The inputSchema property defines the schema for the tool's input parameters.

   • The call function is called when the LLM uses the tool.

2. MCP (Model Context Protocol):

   • The code includes classes and functions for implementing the Model Context Protocol (FN1, gN1).

   • The FN1 class handles communication with an MCP server, including sending requests, receiving responses, and managing notifications.

3. Feature Flags:

   • The code uses feature flags to control the availability of certain features.

   • The NY function retrieves feature flag configurations from the server.

4. Telemetry:

   • The code uses the I0 function to log telemetry events.

   • The telemetry events are used to track the usage of the CLI program.

5. Error Handling:

   • The code includes error handling for LLM calls and other operations.

   • The ls function retries LLM calls if they fail with a retriable error.

6. Code Style:

   • The code includes logic for enforcing code style guidelines.

7. Permissions:

   • The code includes permission checks for tool usage.

   • The needsPermissions property in the tool definitions specifies whether the tool requires permissions.

8. Data Validation:

   • The code uses the zod library to validate the input to the tools.

   • The inputSchema property in the tool definitions specifies the schema for the tool's input parameters.

This analysis should give you a solid foundation for understanding the Claude Code CLI program and how it interacts with LLMs. Remember that this is based solely on the provided code fragment, and a full understanding would require analyzing the entire codebase.

Based on the provided code, here's an analysis of how the /compact instruction works, focusing on prompt design, LLM calling, and related logic:

• The code confirms that /compact is a "local" command (meaning it's handled directly by the CLI and doesn't necessarily involve a separate service).
• The FC9 object defines the behavior of the /compact command.

Prompt Design:

• The core of the /compact command is to generate a summary of the conversation.

• The prompt used for summarization is defined within the FC9.call function:

  w = p9("Provide a detailed but concise summary of our conversation above. Focus on information that would be helpful for continuing the conversation, including what we did, what we're doing, which files we're working on, and what we're going to do next.")
  

  This prompt instructs the LLM to:

  • Provide a detailed but concise summary.

  • Focus on helpful information for continuing the conversation.

  • Include what was done, what is currently being done, files being worked on, and what the next steps are.

• The summarized conversation is then used to set the context for future interactions.

LLM Calling Method:

• The bs function is used to call the LLM to generate the summary.

  B = await bs(sR([...W, w]), ["You are a helpful AI assistant tasked with summarizing conversations."], 0, d, Z.signal, {
            dangerouslySkipPermissions: !1,
            model: G,
            prependCLISysprompt: !0
          })
  

  • sR([...W, w]): This likely prepares the messages for the LLM. W probably represents the existing conversation history, and w is the summarization prompt. sR is likely a function that formats the messages into the structure expected by the LLM API.

  • ["You are a helpful AI assistant tasked with summarizing conversations."]: This is the system prompt provided to the LLM, setting its role for this specific call.

  • 0: This argument is not clear from the context, but it might be related to retry attempts or some other configuration.

  • d: Likely represents the available tools.

  • Z.signal: An AbortSignal, allowing the operation to be cancelled.

  • { dangerouslySkipPermissions: !1, model: G, prependCLISysprompt: !0 }: Options for the LLM call:

    • dangerouslySkipPermissions: !1: Indicates that permission checks should be performed (related to tool usage, perhaps).

    • model: G: Specifies the LLM model to use.

    • prependCLISysprompt: !0: Indicates that a CLI-specific system prompt should be prepended to the messages.

• The response from the LLM is stored in the B variable.

Post-Processing and Context Update:

• After receiving the LLM's response, the code extracts the summary from B.message.content.

• Error handling is present to check for invalid responses or error messages.

• The code then clears the existing conversation history (qx()([])) and sets up a new conversation with the summary as the initial context:

  C([p9("Use the /compact command to clear the conversation history, and start a new conversation with the summary in context."), B])
  

  • C: This is the setForkConvoWithMessagesOnTheNextRender function, which updates the conversation history.

  • p9("Use the /compact command to clear the conversation history, and start a new conversation with the summary in context."): This is a message informing the user that the /compact command was used and that the conversation has been summarized.

  • B: This is the LLM's response containing the summary.

• The j7.cache.clear?.() and aR.cache.clear?.() lines clear the caches related to file system information and code style, respectively. This ensures that the context is refreshed after the conversation is compacted.

Based on the provided code and the insights from previous code fragments, here's what I can tell you about Claude Code's dependency on aws-sdk:

Why aws-sdk is likely used:

The code doesn't directly show the usage of aws-sdk in this fragment. However, the previous fragments suggest that the dependency on aws-sdk stems from interacting with AWS Bedrock.

Related Code Logic:

1. LLM Calling Method:

   • The code uses a Model Context Protocol (MCP) to interact with different LLM providers. The JJ1 function (from previous fragments) initializes different LLM clients based on environment variables (b9, h9). If b9 is true, it initializes a Bedrock client (yr). If h9 is true, it initializes a Vertex AI client (Xa). Otherwise, it initializes a default Claude client (J71).

2. Prompt Design:

   • The code uses a technique to prepend a CLI-specific system prompt (kK2) to the main system prompt if W.prependCLISysprompt is true.

   • The code includes logic to add context to the prompt using <context> tags, based on the d argument in aK2.

3. Other Related Logic:

   • The code defines functions for managing MCP server configurations (AQ2, VQ2, XQ2, YQ2).

   • The code defines functions for approving or rejecting MCP servers (_Q2).

   • The code includes error handling for MCP server connections and requests.

   • The code uses feature flags (NY) to control certain behaviors.

   • The code defines functions to transform messages into the format expected by the API (nZ9).

   • The code defines functions to transform user and assistant messages (cZ9, pZ9).

   • The code defines functions to validate the API key (nK2).

   • The code defines functions to handle API errors and return user-friendly error messages (KJ1).

   • The code defines functions to calculate the retry delay based on the attempt number and the retry-after header (kZ9).

   • The code defines functions to determine whether an API call should be retried based on the error status and headers (xZ9).

   • The code defines functions to log API call information using the I0 function.

   • The code defines functions to retrieve beta features using the fb function and includes them in the API call.

   • The code defines functions to set the max_tokens parameter for the API call based on the model and the requested number of tokens.

   • The code defines functions to include tool descriptions in the API call.

   • The code defines functions to set the metadata parameter for the API call, including user ID.

   • The code defines functions to set the signal parameter for the API call, allowing it to be aborted.

   • The code defines functions to log API call success and error events using the I0 function.

   • The code defines functions to calculate the cost of the API call based on token usage and pricing information.

   • The code defines functions to transform the content of the API response using the hs function.

   • The code defines functions to return the API response with additional information, such as cost and duration.

   • The code defines functions to handle API errors and return a user-friendly error message (KJ1).

   • The code defines functions to transform the messages into the format expected by the API (nZ9).

   • The code defines functions to call the LLM without tool calling (rZ9).

In summary, this code fragment provides further evidence that Claude Code interacts with different LLM providers, including the potential use of AWS Bedrock, which would explain the aws-sdk dependency. The code also reveals details about prompt construction, API calling, error handling, and pricing.