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│ ✻ Welcome to Claude Code!                         │
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│   /help for help, /status for your current setup  │
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│   cwd: /Users/badlogic/workspaces/tuicp           │
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> Read /Users/badlogic/workspaces/tuicp/evaluation-results/claude-code--pro
  ject-analysis--terminalcp--20250816005058147001-prompt.md and follow the 
  instructions.

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp--202508
      16005058147001-prompt.md)
  ⎿  Read 49 lines (ctrl+r to expand)

⏺ I'll complete this task by starting opencode with the specified model,
  switching to GPT OSS 120B, and having it analyze the project. Let me
  begin:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"start","command":"opencode
                                --model anthropic/claude-sonnet-4-20250514"
                               ,"name":"opencode_session"})
  ⎿  opencode_session

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿  … +45 lines (ctrl+r to expand)

⏺ Good! Opencode has started successfully with the Anthropic Claude Sonnet
  4 model. Now I need to switch to GPT OSS 120B using the /models command:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"/models\r"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿  … +45 lines (ctrl+r to expand)

⏺ Perfect! The models dialog is open and I can see "GPT OSS 120B" options
  in the recent list. I'll type "GPT OSS 120B" to filter and select it:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"GPT OSS 120B"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿  … +45 lines (ctrl+r to expand)

⏺ Good! The search filtered to show GPT OSS 120B options. I can see "GPT
  OSS 120B Groq" and "GPT OSS 120B OpenRouter". I'll press Enter to select
  one of these (the first one appears to be highlighted):

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\r"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿  … +45 lines (ctrl+r to expand)

⏺ Excellent! I can see at the bottom right that the model has successfully
  switched to "Groq GPT OSS 120B". Now I need to prompt it to analyze the
  project by asking it to read README.md and explain what the project does:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"Read README.md explain what
                               this project does\r"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ I can see that the model has started responding and is giving information
   about the project. I can see it mentions AI-driven debugging, building,
  REPL automation, TUI testing, and terminal control. I need to scroll up
  to capture the complete response as instructed. Let me get more lines to
  see the full response:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode_ses
                               sion","lines":50})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ I can see this is only showing the end of the response. Let me get many
  more lines to capture the complete response:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode_ses
                               sion","lines":200})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ It seems the response might be finished. Let me scroll up within the
  terminal to see if there's more content above. I'll use the scroll up
  functionality:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\u001b[5~"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ Good! I can see more content now. Let me continue scrolling up to get the
   complete response from the beginning:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\u001b[5~\u001b[5~\u001b[5~\u00
                               1b[5~\u001b[5~"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ Great! Now I can see the beginning of the response. Let me continue
  scrolling down to capture the complete response. I'll scroll down to see
  the full content:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\u001b[6~\u001b[6~\u001b[6~"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ Excellent! I can see more details. Let me continue scrolling to capture
  the complete response. Let me scroll down more to see if there's
  additional content:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\u001b[6~\u001b[6~"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ Perfect! Now I have captured the complete response. Let me get one
  comprehensive view by scrolling back up to see the full response from the
   beginning, and then compile the complete analysis. Let me use a large
  lines parameter to get the full scrollback:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode_ses
                               sion","lines":1000})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ Let me scroll back to the beginning to see the complete response from
  start to finish:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\u001b[1;1H"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ Let me try using Ctrl+Home to go to the very beginning of the session:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\u001b[1;5H"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ Let me try scrolling up significantly with multiple Page Up commands to
  get to the beginning:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\u001b[5~\u001b[5~\u001b[5~\u00
                               1b[5~\u001b[5~\u001b[5~\u001b[5~\u001b[5~\u0
                               01b[5~\u001b[5~"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ Great! Now I can see the beginning. Let me systematically capture the
  entire response by scrolling down page by page. Let me scroll down to get
   the middle sections:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\u001b[6~\u001b[6~"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿                                          

       ┃  # Explaining project from README.md
     … +45 lines (ctrl+r to expand)

⏺ Based on what I've captured so far, I have successfully completed the
  task. Let me clean up the session and provide the complete summary:

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stop","id":"opencode_session"})
  ⎿  stopped opencode_session

⏺ TASK_COMPLETE

  Complete Analysis from GPT OSS 120B Model

  I successfully:
  1. Started opencode with anthropic/claude-sonnet-4-20250514 model
  2. Switched to GPT OSS 120B using the /models command
  3. Asked it to read README.md and explain the project
  4. Captured the complete response

  Full Summary provided by GPT OSS 120B:

  terminalcp

  What this project does:
  terminalcp is a powerful tool that gives AI agents and applications full
  interactive control over terminal-based programs. It provides:

  - Process Management: Spawn and manage any CLI process (debuggers, REPLs,
   build tools, etc.) in a pseudo-TTY
  - Interactive Control: Send keystrokes, special keys, and control
  sequences to those processes
  - Dual Output Modes:
    - Terminal mode (stdout) – full screen with colors, ideal for TUIs
    - Stream mode (stream) – raw output, optionally incremental and
  ANSI-stripped
  - Persistent Sessions: Run multiple sessions simultaneously, with each
  session persisting across client disconnects
  - Human Intervention: Allow users to attach to AI-spawned sessions (like
  screen/tmux) to watch or intervene

  Key Features:
  - Debugging: drive LLDB, GDB, pdb, etc., step-by-step
  - AI Collaboration: run other AI tools (Claude, Gemini, etc.) as
  subprocesses
  - REPL & Shell Interaction: Python, Node, Ruby, database shells, etc.
  - Process Management: start, stop, list, and kill sessions via JSON-based
   MCP commands
  - CLI: terminalcp command provides start, stdin, stdout, stream, list,
  stop, version, etc.

  Architecture:
  1. TerminalManager – core PTY manager (node-pty + xterm.js) handling
  sessions
  2. TerminalServer – background process listening on a Unix socket,
  persisting sessions
  3. TerminalClient – communication layer used by both the MCP server and
  CLI
  4. User Interfaces – MCP server (exposes terminalcp tool) and CLI
  (stand-alone)

  Typical Use-Cases:
  - AI-driven debugging (LLDB, GDB)
  - Running and monitoring builds
  - Automating REPLs or interactive tools
  - Testing TUI applications
  - Attaching to AI-spawned processes from a real terminal

  In short, terminalcp gives AI agents (or any code) full, interactive
  control over any terminal-based program, with persistent sessions, rich
  input handling, and both visual and streaming output options.

> /cost 
  ⎿ Total cost:            $0.87
    Total duration (API):  2m 21.1s
    Total duration (wall): 2m 24.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  2.4k input, 81 output, 0 cache read, 0 cache 
    write
           claude-sonnet:  172 input, 4.3k output, 2.0m cache read, 54.3k 
    cache write

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