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│ ✻ Welcome to Claude Code!                         │
│                                                   │
│   /help for help, /status for your current setup  │
│                                                   │
│   cwd: /Users/badlogic/workspaces/tuicp           │
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> Read /Users/badlogic/workspaces/tuicp/evaluation-results/claude-code--pro
  ject-analysis--terminalcp--20250816004932691000-prompt.md and follow the 
  instructions.

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

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp--202508
      16004932691000-prompt.md)
  ⎿  Read 49 lines (ctrl+r to expand)

⏺ I'll complete this task using the Terminalcp tool to start opencode,
  switch to GPT OSS 120B, and analyze the project.

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

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

⏺ Let me wait a moment for opencode to start up and then check the output:

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

⏺ Great! Opencode has started. Now I'll use the /models command to switch
  to GPT OSS 120B:

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

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

⏺ Perfect! The models dialog is open. I can see "GPT OSS 120B OpenRouter"
  in the list. I'll type "GPT OSS 120B" to filter and then select it:

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

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

⏺ Great! The filter worked and now I can see "GPT OSS 120B OpenRouter" in
  the filtered list. I'll press Enter to select it:

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

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

⏺ Excellent! I can see that the model has been successfully switched to
  "Groq GPT OSS 120B" as shown in the bottom right corner. Now I'll prompt
  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"})
  ⎿  

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

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

⏺ I can see the response is being generated. Let me wait for it to complete
   and then capture the full response:

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

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

⏺ The response appears to be complete. 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","l
                               ines":100})
  ⎿                                          

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

⏺ I need to scroll up to capture the complete response. Let me use a much
  larger line count to get the full analysis:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode","l
                               ines":200})
  ⎿                                          

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

⏺ Let me try using the stream action to get the complete raw output since
  the beginning:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stream","id":"opencode"})
  ⎿  4;1m1u q b                     
      b
      b
     … +210 lines (ctrl+r to expand)

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

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

⏺ TASK_COMPLETE

  Full Model Response Summary

  The GPT OSS 120B model successfully analyzed the project after reading
  README.md and provided the following comprehensive explanation:

  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), handles I/O, ANSI, and provides the API.
  2. TerminalServer – background process that holds all sessions, listening
   on a Unix-socket (~/.terminalcp/server.socket). Sessions persist across
  client disconnects.
  3. TerminalClient – used by both the CLI and the MCP server to talk to
  the server over the socket.
  4. User interfaces – MCP server (exposes a single terminalcp tool) and a
  CLI (terminalcp command). Both use the same client-server protocol.

  MCP usage – send JSON commands (e.g.,
  {"action":"start","command":"python3 -i","name":"py"}) to start/stop
  processes, send input (stdin), get output (stdout/stream), list sessions,
   etc.

  CLI usage – terminalcp start <name> "<command>", terminalcp stdin <name> 
  "cmd" ::Enter, terminalcp stdout <name>, terminalcp stream <name> 
  --since-last, etc.

  Typical use-cases:
  • Debug code step-by-step with LLDB/GDB.
  • Run other AI agents as subprocesses.
  • Drive REPLs, database shells, or any interactive TUI.
  • Monitor long-running builds or logs.
  • Attach from a real terminal to watch or intervene in AI-spawned
  sessions.

  Development:
  • npm install → install deps.
  • npm run build → build.
  • npm test → run tests.

  License – MIT.

> /cost 
  ⎿ Total cost:            $0.55
    Total duration (API):  1m 16.9s
    Total duration (wall): 1m 24.2s
    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:  150 input, 2.6k output, 1.0m cache read, 54.3k 
    cache write

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