Linux Terminal Tool Calling Dataset (Linux-terminal-tool-calling)

This dataset is designed for training and fine-tuning AI agents on tool calling, reasoning, and command execution specifically for standard Linux terminal utilities and system administration tasks. It transforms raw Linux terminal command records into a structured multi-turn conversation format featuring detailed chain-of-thought/reasoning content and OpenAI/OpenClaw-style function calling.

Dataset Details

• Total Records: 600
• Language: English
• Format: JSONL (JSON Lines)
• License: Apache 2.0
• Repository: iselabvn/Linux-terminal-tool-calling

Dataset Structure

Each record is formatted as a single-turn conversation with user and assistant roles, complemented by rich metadata for downstream filtering and analysis.

Field Descriptions

• messages (list): Conversation history.
  • role: "user" (dict): The user request describing a Linux terminal or system administration task.
  • role: "assistant" (dict): The assistant response containing:
    • reasoning_content (str): A 2-3 sentence chain-of-thought explanation explaining the choice of command, flags, and parameter configurations.
    • tool_calls (list): An array containing the function call. The tool utilizes the exec function to run the command on the target environment.
      • id (str): A unique call identifier (e.g., call_exec_0).
      • type: "function" (str): The type of tool call.
      • function (dict): Details of the target function call.
        • name: "exec"
        • arguments (JSON string): Serialized dictionary containing the exact executable command ({"command": "..."}).
    • content (str): Empty string (standard format for tool calling responses).
• metadata (dict): Metadata associated with the command execution.
  • id (str): Unique command ID (e.g., cmd-001).
  • category (str): Functional category of the command (e.g., File Management, Viewing, System Info).
  • example_output (str): Sample execution output or return status.
  • man_reference (str): Link to the official man page documentation.
  • original_description (str): The original, generic user description before prompt synchronization.

Category Distribution

Category	Record Count	Description
File Management	94	File creation, copying, moving, deletion, and file system operations
Viewing	90	Displaying file contents, tailing log files, viewing headers & line counts
System Info	79	System resources, hardware details, uptime, kernel & OS information
Networking	76	Network interfaces, routing, connectivity tests, DNS lookups, ports
Process	65	Process listing, monitoring, signaling, background jobs, termination
Permissions	50	File and directory permissions, ownership, and ACL modifications
User Management	46	User/group management, password policies, and shell configurations
Package Management	39	APT/YUM/RPM package management, updates, and software installations
Navigation	35	Directory traversal, path printing, and directory listing
Editor	26	CLI text editor invocations and stream processing (nano, vim, sed, awk)

Sample Record

{
  "messages": [
    {
      "role": "user",
      "content": "Change the current working directory to /var/log."
    },
    {
      "role": "assistant",
      "reasoning_content": "The user wants to navigate to the /var/log directory. The 'cd' command is the standard way to change directories in Unix-like systems. The path /var/log is specified directly. Executing the tool.",
      "tool_calls": [
        {
          "id": "call_exec_0",
          "type": "function",
          "function": {
            "name": "exec",
            "arguments": "{\"command\": \"cd /var/log\"}"
          }
        }
      ],
      "content": ""
    }
  ],
  "metadata": {
    "id": "cmd-001",
    "category": "Navigation",
    "example_output": "[No output, changes directory]",
    "man_reference": "https://man7.org/linux/man-pages/man1/cd.1.html",
    "original_description": "Changes the current directory to /var/log."
  }
}

Intended Use

This dataset is ideal for:

• Fine-tuning language models to act as autonomous agent loops in Linux terminal environments.
• Training models on standard OpenAI tool calling conventions for shell commands.
• Supervised Fine Tuning (SFT) for system administration assistants, incorporating chain-of-thought (reasoning) before issuing commands.

Construction Method

The dataset was constructed by converting raw Linux terminal command records into a structured tool-use conversation trace.

To ensure consistency between user requests and executable commands, we utilized internal LLMs to perform Prompt Synchronization:

1. Target Injection: Generic references (e.g., "a directory", "a file") in user requests were automatically replaced or synchronized with specific target parameters found in the command (e.g., /var/log, logfile.txt).
2. Chain-of-Thought Synthesis: The LLM generated a 2-3 sentence reasoning_content to justify the selection of the command, flags, and arguments.
3. Metadata Preservation: Original command IDs, categories, man references, sample outputs, and descriptions are preserved in metadata for alignment and verification.