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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--20250816010511768009-prompt.md and follow the 
  instructions.

⏺ I'll read the file you specified to understand the instructions.

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp--202508
      16010511768009-prompt.md)
  ⎿  Read 49 lines (ctrl+r to expand)

⏺ I'll complete this task using the Terminalcp tool. I need to:

  1. Start opencode with the specified model
  2. Switch to GPT OSS 120B using /models command
  3. Ask it to analyze the project by reading README.md
  4. Capture the complete response

  Let me start:

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

⏺ Now let me check the terminal to see if opencode has started:

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

⏺ Let me wait a moment for opencode to initialize and then check again:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode_ses
                               sion","lines":50})
  ⎿  … +45 lines (ctrl+r to expand)

⏺ Great! Opencode has started and is showing the main interface. Now I need
   to use the /models command to switch to GPT OSS 120B:

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

⏺ Let me check the output to see the models dialog:

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

⏺ Perfect! I can see the models dialog is open and "GPT OSS 120B Groq" is
  visible in the recent models. I need to type "GPT OSS 120B" to filter and
   select it. Let me type that:

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

⏺ Let me check the filtered results:

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

⏺ Good! The filter is working and showing GPT OSS 120B options. I can see
  "GPT OSS 120B Groq" is highlighted at the top. Now I need to press Enter
  to select it:

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

⏺ Let me check if the model has been switched:

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿  … +44 lines (ctrl+r to expand)

⏺ Excellent! I can see at the bottom right that the model has been
  successfully switched to "Groq GPT OSS 120B". Now I need to ask it to
  analyze the project by reading the README.md file:

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

⏺ Let me check the terminal to see the model's response:

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

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

⏺ I can see the model is responding and has started analyzing the project.
  I can see it's showing the architecture information. Let me wait for the
  complete response and then capture it. Let me check again to see if more
  content has appeared:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode_ses
                               sion","lines":100})
  ⎿                                          

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

⏺ Good! I can see the response is complete (the status bar shows "enter
  send" instead of "w.. ing" which indicates it's done processing). Now I
  need to scroll up to capture the complete response. Let me get more lines
   to see the full output:

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

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

⏺ I notice the response appears to be cut off. Let me scroll up in the
  terminal to see if there's more content above. I need to scroll up to
  capture the beginning of the response:

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

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

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

⏺ I can see more content now. Let me continue scrolling up to see the
  beginning of the response:

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

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

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

⏺ Great! Now I can see the beginning of the response. Let me scroll down to
   capture the complete response by getting the full terminal output. Let
  me use the stream option to get all the output:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stream","id":"opencode_ses
                               sion","since_last":false,"strip_ansi":true})
  ⎿  4;1m1u q b                     
      b
      b
     … +200 lines (ctrl+r to expand)

⏺ Perfect! Now I have captured the complete response. Let me clean up the
  session and provide the full summary:

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

⏺ TASK_COMPLETE

  I successfully completed the task. Here is the COMPLETE response from GPT
   OSS 120B after analyzing the README.md:

  ---
  terminalcp

  Let AI agents control interactive command-line tools like a human would.

  Project Overview – terminalcp

  Purpose - terminalcp lets AI agents (or any program) control interactive
  command-line tools just like a human would. It's essentially "Playwright
  for the terminal": the agent can start a process, send keystrokes, read
  output, and keep the session alive, all while preserving full terminal
  emulation (colors, cursor movement, etc.).

  Key capabilities

  • Debugging – drive LLDB, GDB, pdb, etc.
  • AI-to-AI – run other AI assistants (Claude, Gemini, etc.) as
  subprocesses.
  • REPL / shell – interact with Python, Node, Ruby REPLs, DB shells,
  system monitors.
  • General CLI – any interactive CLI that expects human input.
  • Multiple processes – run many sessions simultaneously without blocking.
  • Attach – users can attach to AI-spawned sessions from their own
  terminal (like screen/tmux) to watch or help.

  Two output modes

  • Terminal mode (stdout) – returns the full rendered screen (good for
  TUIs, debuggers).
  • Stream mode – raw output, optional ANSI stripping, incremental reading
  (good for logs, builds).

  Architecture

  1. TerminalManager – core library that creates PTY sessions (via
  node-pty), emulates a terminal (xterm.js headless), and handles I/O.
  2. TerminalServer – background process that listens on a Unix-domain
  socket, manages all sessions, and persists them across client
  disconnects.
  3. TerminalClient – used by both the CLI and the MCP server to talk to
  the server.
  4. User interfaces – MCP server (exposes a single terminalcp tool) and a
  CLI (terminalcp command).

  MCP usage – send JSON commands (start, stdin, stdout, stream, stop, etc.)
   to the server; responses are plain text.

  CLI usage – terminalcp start, terminalcp attach, terminalcp stdin,
  terminalcp stdout, terminalcp stream, etc.

  Typical use-cases

  • Debug code step-by-step with LLDB/GDB.
  • Run other AI agents as subprocesses.
  • Drive REPLs, TUI apps (vim, htop), or any interactive CLI.
  • Monitor long-running processes (builds, logs).

  Development – npm install, npm run build, npm test.

  License – MIT.

  ---
  The task was successfully completed: I started opencode with the
  specified model, switched to GPT OSS 120B, and captured the complete
  analysis of the project.

> /cost 
  ⎿ Total cost:            $0.61
    Total duration (API):  1m 20.7s
    Total duration (wall): 1m 24.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  2.4k input, 72 output, 0 cache read, 0 cache 
    write
           claude-sonnet:  146 input, 3.0k output, 1.2m cache read, 51.8k 
    cache write

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