README.md

---
configs:
- config_name: default
  data_files:
  - split: train
    path: linux_terminal_tool_calling_dataset.jsonl
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- tool-calling
- linux
- terminal
- openclaw
- reasoning
- agent
- sysadmin
size_categories:
- n<1K
---

# 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](https://huggingface.co/datasets/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

```json
{
  "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.