Prompt Engineering for Claude Code

The prompt engineering in Claude Code does not rely on a single "ultimate System Prompt," but rather on a layered, dynamically assembled prompt system. This architecture decouples basic identity, session state, runtime environment, tool specifications, and multi-agent collaboration rules into independent modules, dynamically assembling them at execution time based on the current context. Understanding this architecture is key to mastering Claude Code's behavioral logic, routing strategies, and custom extension mechanisms.

The most core prompt entry point for Claude Code is located in constants/prompts.ts. The source code defines the most basic initial guidance section:

function getSimpleIntroSection(outputStyleConfig: OutputStyleConfig | null): string {
  return `
You are an interactive agent that helps users ${
  outputStyleConfig !== null
    ? 'according to your "Output Style" below, which describes how you should respond to user queries.'
    : 'with software engineering tasks.'
} Use the instructions below and the tools available to you to assist the user.

IMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming.`
}

English Translation: You are an interactive agent responsible for helping users complete software engineering tasks (or responding according to the "Output Style" configuration below). Please use the instructions and available tools below to assist the user. Important: Unless you are certain a URL is genuinely meant to help the user with a programming task, you must never generate or guess URLs for the user. This Prompt strictly defines four layers of logic:

• Establishing basic identity: Interactive Agent (not a pure chatbot)
• Anchoring the task domain: Software engineering
• Stating interaction prerequisites: Tools are available
• Setting security boundaries: Strictly prohibit baseless URL generation This forms the foundational prompt for Claude Code, setting the scope and limits for all subsequent behavior.

Claude Code refuses to hardcode the system prompt into a single, massive fixed template, and instead dynamically concatenates multiple independent sections at runtime:

const dynamicSections = [
  systemPromptSection('session_guidance', () =>
    getSessionSpecificGuidanceSection(enabledTools, skillToolCommands),
  ),
  systemPromptSection('memory', () => loadMemoryPrompt()),
  systemPromptSection('env_info_simple', () =>
    computeSimpleEnvInfo(model, additionalWorkingDirectories),
  ),
  systemPromptSection('language', () =>
    getLanguageSection(settings.language),
  ),
  systemPromptSection('output_style', () =>
    getOutputStyleSection(outputStyleConfig),
  ),
  DANGEROUS_uncachedSystemPromptSection(
    'mcp_instructions',
    () => isMcpInstructionsDeltaEnabled() ? null : getMcpInstructionsSection(mcpClients),
    'MCP servers connect/disconnect between turns',
  ),
]

This mechanism includes at least the following core modules:

• Session guidance (session_guidance)
• Memory layer (memory)
• Basic environment information (env_info_simple)
• Language preference (language)
• Output style configuration (output_style)
• MCP server connection instructions (mcp_instructions)

Execution Logic Comparison: ❌ Traditional approach: Pasting a fixed system prompt into the model. ✅ Claude Code's mechanism: Dynamically assembling the most appropriate system prompt for this round based on the current session state. This is precisely why its level of engineering rigor far exceeds that of standard "copy-paste prompt" AI tools.

The CLI layer exposes two explicit prompt injection parameters in main.tsx:

addOption(new Option('--system-prompt <prompt>', 'System prompt to use for the session').argParser(String))
addOption(new Option('--append-system-prompt <prompt>', 'Append a system prompt to the default system prompt').argParser(String))

The underlying routing logic is strictly differentiated in utils/queryContext.ts:

// customSystemPrompt replaces the default system prompt entirely.
// appendSystemPrompt appends extra text after the default system prompt.

customSystemPrompt completely replaces the default system prompt. appendSystemPrompt appends additional content after the default system prompt.

Engineering Value: Most practical use cases do not seek to "overturn default prompts," but rather to "layer constraints on top of existing capabilities." Distinguishing between replace and append is a classic example of defensive design, simultaneously enabling deep customization while maintaining baseline stability.

In the multi-agent collaboration (Agent Swarms) mode, the system automatically injects dedicated communication specifications into nodes. When a team session is detected, main.tsx triggers supplemental logic:

if (isAgentSwarmsEnabled() && storedTeammateOpts?.agentId && storedTeammateOpts?.agentName && storedTeammateOpts?.teamName) {
  const addendum = getTeammatePromptAddendum().TEAMMATE_SYSTEM_PROMPT_ADDENDUM;
  appendSystemPrompt = appendSystemPrompt ? `${appendSystemPrompt}

${addendum}` : addendum;
}

The actual appended content is defined in utils/swarm/teammatePromptAddendum.ts:

export const TEAMMATE_SYSTEM_PROMPT_ADDENDUM = `
# Agent Teammate Communication

IMPORTANT: You are running as an agent in a team. To communicate with anyone on your team:
- Use the SendMessage tool with \`to: "<name>"\` to send messages to specific teammates
- Use the SendMessage tool with \`to: "*"\` sparingly for team-wide broadcasts

Just writing a response in text is not visible to others on your team - you MUST use the SendMessage tool.
`

You are running as a Teammate Agent within the team. If you need to communicate with other members of your team: Use the `SendMessage` tool and set `to` to the specific member's name; only when necessary use `to: "*"` for all-team broadcasts. Simply outputting standard text is not visible to others on the team — you MUST use the SendMessage tool. Essence: This falls under role-switching and permission-boundary declarations rather than knowledge-based prompts. It forces the model to clarify its current Identity, Visibility Radius (Boundary of Interaction), and Interaction Protocol.

The tools in Claude Code provide not only the JSON Schema but also built-in, tightly constrained natural language explanations that directly intervene in the quality of the model's tool-calling decisions.

tools/FileReadTool/prompt.ts:

return `Reads a file from the local filesystem. You can access any file directly by using this tool.
Assume this tool is able to read all files on the machine. If the User provides a path to a file assume that path is valid.

Usage:
- The file_path parameter must be an absolute path, not a relative path
- By default, it reads up to 2000 lines starting from the beginning of the file
- This tool can only read files, not directories. To read a directory, use an ls command via the Bash tool.
- You will regularly be asked to read screenshots. If the user provides a path to a screenshot, ALWAYS use this tool to view the file at the path.`

The value of this Prompt does not lie merely in stating "that the Read tool exists," but rather in precisely defining parameter specifications (must be an absolute path), read limits (default 2000 lines), boundary conditions (reading files only; reading directories requires using the Bash ls command), and multimodal interception rules (screenshot paths must strictly invoke this tool).

tools/BashTool/prompt.ts contains a significant number of Shell behavior constraints, including one key directive:

Do NOT use the Bash tool to run commands when a relevant dedicated tool is provided.

When a more suitable dedicated tool is already provided, do not use the Bash tool to execute commands. This rule directly governs the model's tool routing strategy: prioritize `Read` for file access → prioritize `Grep` for text search → prioritize `Glob` for path matching → and only invoke `Bash` as a last resort. Tool Prompts here serve as an implicit strategy controller.

The Plan Mode also possesses its own independent constraint Prompt. Its core objective is not code generation, but rather standardizing "when planning may conclude" and "when to return decision-making to the user," ensuring that tool lifecycles occurring outside the main conversation remain properly controlled.

Behind the scenes, Claude Code runs an implicit Prompt network not directly exposed in the main window, specifically to support lateral task flows:

• /init Initialization Directive: Rather than randomly generating a CLAUDE.md, it calls upon a specialized Prompt to force the model to structure its output regarding project architecture, execution methods, common commands, coding standards, and collaboration constraints — essentially automating Onboarding.
• Tool Summary / Prompt Suggestion: Used to compress cluttered Tool Call results and generate guiding suggestions for subsequent rounds. These prompts focus on internal system context scheduling and redundancy reduction; although invisible to the user, they directly determine interaction fluency.

• Not static text, but a layered runtime: Core prompts, dynamic composition layers, tool constraints, and role specifications are decoupled and dynamically recombined in real time according to session state.
• Strict routing strategy control: Tool-embedded prompts directly steer the model's decision tree (e.g., Bash fallback policies, Read's multimodal interception).
• Separate design for replace vs append: The CLI explicitly distinguishes between “overwriting the baseline” and “stacking constraints,” balancing deep customization with system stability.
• The implicit scheduling layer dictates the experience: Background prompts such as /init, Tool Summary, and Prompt Suggestion drive initialization quality and context efficiency despite being invisible to the user.
• Architecture evolution trend: Claude Code has elevated prompt engineering from “text orchestration” to “modular runtime scheduling,” laying the foundational groundwork for complex agent collaboration.