╭───────────────────────────────────────────────────╮
│ ✻ Welcome to Claude Code!                         │
│                                                   │
│   /help for help, /status for your current setup  │
│                                                   │
│   cwd: /Users/badlogic/workspaces/tuicp           │
╰───────────────────────────────────────────────────╯

> Read /Users/badlogic/workspaces/tuicp/evaluation-results/claude-code--pro
  ject-analysis--terminalcp--20250816010401351008-prompt.md and follow the 
  instructions.

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

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp--202508
      16010401351008-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.

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

⏺ Now I'll wait a moment for opencode to start and then check the current
  state:

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

⏺ Great! opencode 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","da
                               ta":"/models\r"})
  ⎿  

⏺ Let me check the current state to see the models dialog:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode"})
  ⎿  … +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" and "GPT OSS 120B
  OpenRouter" in the list. I'll type "GPT OSS 120B" to filter and then
  select one:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"GPT OSS 120B"})
  ⎿  

⏺ Let me check the filtered results:

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

⏺ I can see the filtered results showing GPT OSS 120B models. I'll press
  Enter to select the first one (GPT OSS 120B Groq):

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\r"})
  ⎿  

⏺ Let me check if the model has been switched:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode"})
  ⎿  … +45 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'll ask it to analyze
  the project by reading the README.md:

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

⏺ Let me check the response:

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

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

⏺ I can see the model is responding. Let me wait a bit longer for the
  complete response and then capture it with more lines to get the full
  analysis:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode","l
                               ines":50})
  ⎿                                          

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

⏺ Let me scroll up to capture the complete response from the beginning:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode","l
                               ines":100})
  ⎿                                          

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

⏺ I need to scroll up further to capture the beginning of the response. Let
   me use the stream action to get the complete raw output:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stream","id":"opencode","s
                               trip_ansi":true})
  ⎿  4;1m1u q b                     
      b
      b
     … +201 lines (ctrl+r to expand)

⏺ Perfect! I can see the complete response. The GPT OSS 120B model has
  successfully analyzed the project. Let me now stop the session and
  provide the complete summary:

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

⏺ TASK_COMPLETE

  I successfully completed the task. Here is the full analysis provided by
  GPT OSS 120B after switching from Claude Sonnet 4:

  Project Overview – terminalcp

  terminalcp is a Node.js library and CLI 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 interface 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 (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 a simple
  JSON-based MCP tool.
  • 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, list,
  stop, version, etc.
  • Programmatic API: TerminalManager class for programmatic control
  (start, sendInput, stdout, stream, stop).

  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 from a real terminal to watch or help AI-spawned processes.

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

  The model switch to GPT OSS 120B was successful, and the complete
  response was captured as requested.

> /cost 
  ⎿ Total cost:            $0.4895
    Total duration (API):  1m 4.9s
    Total duration (wall): 1m 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:  126 input, 2.3k output, 905.4k cache read, 48.7k
     cache write

╭──────────────────────────────────────────────────────────────────────────────╮
│ >                                                                            │
╰──────────────────────────────────────────────────────────────────────────────╯
  ⏵⏵ bypass permissions on (shift+tab to cycle)                 Auto-updating…