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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--20250816010000453006-prompt.md and follow the 
  instructions.

⏺ I'll read the file and follow the instructions in it.

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp--202508
      16010000453006-prompt.md)
  ⎿  Read 49 lines (ctrl+r to expand)

⏺ I'll follow the instructions to start opencode with the specified model,
  switch to GPT OSS 120B, and have it analyze the project. Let me begin by
  starting the opencode session.

⏺ 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"})
  ⎿  

⏺ Let me wait a moment for the session to initialize and try again:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode_ses
                               sion","lines":20})
  ⎿                        █░░█ █░░█ █▀▀ █░░█ █░░ █░░█ █░░█ █▀▀

                           ▀▀▀▀ █▀▀▀ ▀▀▀ ▀  ▀ ▀▀▀ ▀▀▀▀ ▀▀▀  ▀▀▀
     … +37 lines (ctrl+r to expand)

⏺ Great! The opencode session has started successfully. 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"})
  ⎿  

⏺ 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.
  I need to type "GPT OSS 120B" to filter the list and then 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)

⏺ I can see that the list has been filtered and shows "GPT OSS 120B Groq"
  and "GPT OSS 120B OpenRouter". Now I need to press Enter to select the
  first one (GPT OSS 120B Groq):

⏺ 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 the model has switched successfully - the bottom
  right now shows "Groq GPT OSS 120B" instead of "Anthropic Claude Sonnet
  4". Now I need to prompt it to analyze the project by asking it to read
  the README.md:

⏺ 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)

⏺ The model is working on the analysis. I can see some output already about
   terminal modes and architecture. Let me wait a bit more for the complete
   response:

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

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

⏺ Good, the model seems to be done with the analysis. I can see it has
  completed the response. Now I need to capture the complete response by
  scrolling up to see all the output. Let me get more lines to capture the
  full response:

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

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

⏺ I can see the response is complete but I need to capture more of it by
  scrolling up to get the full analysis. Let me scroll up to see 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":100})
  ⎿                                          

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

⏺ 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_ses
                               sion","lines":100})
  ⎿                                          

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

⏺ Good! Now I can see the beginning of the response. Let me continue
  scrolling down to capture the full analysis systematically. Let me go
  back to the end and then capture the complete response:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode_sess
                               ion","data":"\u001b[6~\u001b[6~\u001b[6~\u00
                               1b[6~\u001b[6~\u001b[6~\u001b[6~\u001b[6~\u0
                               01b[6~\u001b[6~"})
  ⎿  

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

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

⏺ Let me use the stream output to get the complete raw response from the
  beginning:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stream","id":"opencode_ses
                               sion","strip_ansi":true})
  ⎿  4;1m1u q b                     
      b
      b
     … +248 lines (ctrl+r to expand)

⏺ Perfect! I have captured the complete response from GPT OSS 120B. Now let
   me clean up by stopping the session and provide the complete analysis.

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

⏺ TASK_COMPLETE

  I successfully completed the task. Here is the full analysis provided by
  GPT OSS 120B after switching models and asking it to read README.md:

  Complete Response from GPT OSS 120B:

  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 model switch to GPT OSS 120B was successful and the complete project
  analysis was captured.

> /cost 
  ⎿ Total cost:            $0.66
    Total duration (API):  1m 29.0s
    Total duration (wall): 1m 34.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  266 input, 57 output, 0 cache read, 0 cache 
    write
           claude-sonnet:  162 input, 3.3k output, 1.4m cache read, 54.1k 
    cache write

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