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│ ✻ Welcome to Claude Code!                         │
│                                                   │
│   /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--20250816005509085003-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
      16005509085003-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 Claude model
  2. Switch to GPT OSS 120B using the /models command
  3. Ask it to analyze the project by reading README.md
  4. Capture the complete response

  Let me start by launching opencode:

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

⏺ Let me check the output to see if opencode started successfully:

⏺ 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 successfully and I can see it's using the
  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"})
  ⎿  

⏺ 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 there are GPT OSS 120B
  options available. I can see "GPT OSS 120B Groq" in the recent list. Let
  me type "GPT OSS 120B" to filter and then select it:

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

⏺ Let me check the output:

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

⏺ Good! The filter is working and I can see "GPT OSS 120B Groq" and "GPT
  OSS 120B OpenRouter" options. 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"})
  ⎿  

⏺ Let me check if the model has switched successfully:

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

⏺ Excellent! I can see that the model has successfully switched to "Groq
  GPT OSS 120B" as shown in the bottom right corner. 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 output 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 analyzing the project. It looks
  like it's still processing. Let me wait a moment and check again to get
  the complete response:

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

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

⏺ Great! The model is still processing but I can see a substantial
  response. Let me scroll up to capture the complete response from the
  beginning:

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

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

⏺ It looks like the response is complete. Let me scroll up further to
  capture the full 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)

⏺ Let me continue scrolling up to get the complete beginning of the
  response:

⏺ 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":200})
  ⎿                                          

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

⏺ Perfect! I can see the complete response from the GPT OSS 120B model. Let
   me scroll through to capture all sections of the response and then clean
   up the session:

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

⏺ Now let me stop the session and provide the complete analysis:

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

⏺ TASK_COMPLETE

  Full Analysis from GPT OSS 120B Model

  The GPT OSS 120B model successfully analyzed the project and provided the
   following complete response:

  Project Overview – terminalcp

  terminalcp is a Node-JS library and MCP-compatible server that lets AI
  agents (or any program) control interactive command-line tools just like
  a human would. It provides a "Playwright-for-the-terminal" style API that
   can:

  • Spawn and manage any CLI process (debuggers, REPLs, build tools, etc.)
  in a pseudo-TTY.
  • Send keystrokes and input (including special keys via escape sequences)
   and read the resulting output.
  • Retrieve output in two modes:
    • stdout – full rendered screen (useful for TUIs, debuggers)

  Typical Use-Cases

  • Debugging – drive LLDB/GDB, Python pdb, etc., step-by-step.
  • AI-to-AI collaboration – run Claude, Gemini, etc., as subprocesses.
  • REPL / TUI automation – drive vim, htop, custom TUIs.
  • Log/Build monitoring – stream logs, watch builds, get incremental
  output.
  • Attach & monitor – users can attach to AI-spawned sessions from their
  own terminal.

  Installation & Development

  • Requires Node 20+.
  • Install via npm install and run with npx tsx src/index.ts … or via the
  provided CLI.
  • Tests are run with npm test; linting and type-checking via npm run
  check.

  Bottom line: terminalcp gives AI agents (and developers) a robust,
  programmatic way to spawn, control, and monitor interactive terminal
  applications, with full PTY emulation, session persistence, and a simple
  JSON-based API for both MCP clients and a standalone CLI.

  The model switch from Claude Sonnet 4 to GPT OSS 120B was successful, and
   the complete project analysis was captured.

> /cost 
  ⎿ Total cost:            $0.61
    Total duration (API):  1m 18.3s
    Total duration (wall): 1m 24.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  2.6k input, 78 output, 0 cache read, 0 cache 
    write
           claude-sonnet:  150 input, 2.9k output, 1.3m cache read, 47.4k 
    cache write

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