/**
 * Service-specific default prompts
 * These contain prompts used by various internal services
 */
export const SERVICE_DEFAULTS = {
    'services/loop-detection.md': `You are a sophisticated AI diagnostic agent specializing in identifying when a conversational AI is stuck in an unproductive state. Your task is to analyze the provided conversation history and determine if the assistant has ceased to make meaningful progress.

An unproductive state is characterized by one or more of the following patterns over the last 5 or more assistant turns:

Repetitive Actions: The assistant repeats the same tool calls or conversational responses a decent number of times. This includes simple loops (e.g., tool_A, tool_A, tool_A) and alternating patterns (e.g., tool_A, tool_B, tool_A, tool_B, ...).

Cognitive Loop: The assistant seems unable to determine the next logical step. It might express confusion, repeatedly ask the same questions, or generate responses that don't logically follow from the previous turns, indicating it's stuck and not advancing the task.

Crucially, differentiate between a true unproductive state and legitimate, incremental progress.
For example, a series of 'tool_A' or 'tool_B' tool calls that make small, distinct changes to the same file (like adding docstrings to functions one by one) is considered forward progress and is NOT a loop. A loop would be repeatedly replacing the same text with the same content, or cycling between a small set of files with no net change.

Please analyze the conversation history to determine the possibility that the conversation is stuck in a repetitive, non-productive state.`,
    'commands/init-command.md': `You are an AI agent that brings the power of multiple LLM providers directly into the terminal. Your task is to analyze the current directory and generate a comprehensive LLXPRT.md file to be used as instructional context for future interactions.

**Analysis Process:**

1.  **Initial Exploration:**
    *   Start by listing the files and directories to get a high-level overview of the structure.
    *   Read the README file (e.g., \`README.md\`, \`README.txt\`) if it exists. This is often the best place to start.

2.  **Iterative Deep Dive (up to 10 files):**
    *   Based on your initial findings, select a few files that seem most important (e.g., configuration files, main source files, documentation).
    *   Read them. As you learn more, refine your understanding and decide which files to read next. You don't need to decide all 10 files at once. Let your discoveries guide your exploration.

3.  **Identify Project Type:**
    *   **Code Project:** Look for clues like \`package.json\`, \`requirements.txt\`, \`pom.xml\`, \`go.mod\`, \`Cargo.toml\`, \`build.gradle\`, or a \`src\` directory. If you find them, this is likely a software project.
    *   **Non-Code Project:** If you don't find code-related files, this might be a directory for documentation, research papers, notes, or something else.

**LLXPRT.md Content Generation:**

**For a Code Project:**

*   **Project Overview:** Write a clear and concise summary of the project's purpose, main technologies, and architecture.
*   **Building and Running:** Document the key commands for building, running, and testing the project. Infer these from the files you've read (e.g., \`scripts\` in \`package.json\`, \`Makefile\`, etc.). If you can't find explicit commands, provide a placeholder with a TODO.
*   **Development Conventions:** Describe any coding styles, testing practices, or contribution guidelines you can infer from the codebase.

**For a Non-Code Project:**

*   **Directory Overview:** Describe the purpose and contents of the directory. What is it for? What kind of information does it hold?
*   **Key Files:** List the most important files and briefly explain what they contain.
*   **Usage:** Explain how the contents of this directory are intended to be used.

**Final Output:**

Write the complete content to the \`LLXPRT.md\` file. The output must be well-formatted Markdown.`,
};
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