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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--20250816005634541004-prompt.md and follow the 
  instructions.

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

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp--202508
      16005634541004-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
  start by launching opencode with the anthropic/claude-sonnet-4-20250514
  model.

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

⏺ Let me check the current state of the opencode session:

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

⏺ Let me wait a moment for opencode to fully 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 is now running with the Anthropic Claude Sonnet 4 model.
  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 there are two GPT OSS
  120B options available - one from Groq and one from OpenRouter. I'll type
   "GPT OSS 120B" to filter the list as instructed:

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

⏺ Let me check what happened after typing the filter:

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

⏺ I can see the filtered list now shows the GPT OSS 120B 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 was successfully switched:

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

⏺ Excellent! I can see in the bottom right that the model has successfully
  been switched to "Groq GPT OSS 120B". Now I need to ask it to analyze the
   project by prompting it to read the 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"})
  ⎿  

⏺ 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 the response is still being generated. Let me wait a moment and
  check again to capture the complete response:

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

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

⏺ It looks like the response is complete now. Let me scroll up to capture
  the full response by getting more lines of output:

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

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

⏺ I need to scroll up more to see the beginning of the response. Let me try
   with more lines:

⏺ 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 scroll up further by pressing Page 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_session"})
  ⎿                                          

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

⏺ Let me continue scrolling up to see more of the response:

⏺ 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)

⏺ Let me continue scrolling up to see the beginning:

⏺ 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)

⏺ Let me continue scrolling to see the very beginning:

⏺ 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)

⏺ Perfect! Now I can see the beginning of the response. Let me scroll down
  to see the rest and capture the complete response. Let me go to the end
  and scroll back up systematically:

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

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

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

⏺ Let me try to get the complete response by using the stream action to
  capture all the raw output from the session:

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

⏺ Great! Now I have captured the complete response from the GPT OSS 120B
  model. Let me clean up by stopping the session and then provide the
  complete summary:

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

⏺ TASK_COMPLETE

  I successfully completed the task by:

  1. ✅ Started opencode with the anthropic/claude-sonnet-4-20250514 model
  2. ✅ Used the /models command to switch to GPT OSS 120B (Groq)
  3. ✅ Prompted the model to "Read README.md explain what this project
  does"
  4. ✅ Captured the complete response from GPT OSS 120B

  Complete Response from GPT OSS 120B:

  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, special keys, and control sequences to those
  processes.
  • Read output either as a full rendered screen (for TUIs, debuggers,
  etc.) or as a raw stream (for logs, builds, etc.).
  • Run multiple sessions simultaneously, with each session persisting
  across client disconnects.
  • Allow users to attach to AI-spawned sessions from their own terminal
  (similar to screen/tmux), so they can watch or intervene in real-time.

  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
  commands over MCP.
  • Two output modes:
    • Terminal mode (stdout) – full screen with colors, ideal for TUIs.
    • Stream mode (stream) – raw output, optionally incremental and
  ANSI-stripped.
  • CLI: terminalcp command provides start, stdin, stdout, stream, attach,
  stop, etc.
  • Programmatic API: TerminalManager class for programmatic control
  (start, sendInput, stdout, stream, stop).

  Architecture

  1. TerminalManager – core PTY manager (node-pty + xterm.js) handling I/O
  and terminal emulation.
  2. TerminalServer – background process listening on a Unix socket,
  managing all sessions.
  3. TerminalClient – used by both the MCP server and CLI to talk to the
  server.
  4. User Interfaces – MCP server (exposes a single terminalcp tool) and
  CLI (stand-alone).

  Typical Use-Cases

  • AI-driven debugging (LLDB, GDB).
  • Running and monitoring builds.
  • Driving REPLs or interactive shells.
  • Testing TUI applications.
  • Collaborative AI sessions where multiple users can attach to the same
  terminal.

  In short, terminalcp gives AI agents (and developers) a robust,
  programmatic way to spawn, control, and monitor any interactive terminal
  program, with full PTY emulation and persistent sessions.

> /cost 
  ⎿ Total cost:            $0.79
    Total duration (API):  2m 2.6s
    Total duration (wall): 2m 9.2s
    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:  170 input, 3.7k output, 1.7m cache read, 57.6k 
    cache write

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