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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--20250816010135902007-prompt.md and follow the 
  instructions.

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

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp--202508
      16010135902007-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 ask it to analyze the project. Let me start
  by launching opencode.

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

⏺ Now let me check the current state of the opencode session:

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

⏺ Let me wait a moment for opencode to fully load and then check again:

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

⏺ Perfect! Opencode is running. 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)

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

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

⏺ Let me check the state to see the filtered list:

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

⏺ Perfect! I can see the filtered list with GPT OSS 120B options. I can see
   "GPT OSS 120B Groq" is the first option. Now I'll press Enter to select
  it:

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

⏺ Let me check if the model has been successfully switched:

⏺ 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 at the bottom right of the screen. Now I
  need to ask it to analyze the project by reading 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 output to see the GPT OSS 120B model's 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 has started responding but I need to capture the
  complete response. Let me get more lines to see the full output:

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

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

⏺ It looks like the response is complete but I should scroll up to capture
  the full response from the beginning. Let me scroll up to see the
  complete analysis:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\u001b[5~"})
  ⎿  

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

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

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

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\u001b[5~\u001b[5~\u001b[5~"})
  ⎿  

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

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

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

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\u001b[5~\u001b[5~\u001b[5~\u001b[5~\u0
                               01b[5~"})
  ⎿  

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

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

⏺ Great! I can see the beginning of the response. Now let me scroll down to
   capture the complete analysis:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\u001b[6~"})
  ⎿  

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

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

⏺ Let me continue scrolling down to see the complete response:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\u001b[6~\u001b[6~"})
  ⎿  

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

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

⏺ Let me continue to the end:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\u001b[6~\u001b[6~\u001b[6~"})
  ⎿  

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

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

⏺ Let me get to the end of the response:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\u001b[6~\u001b[6~\u001b[6~\u001b[6~"})
  ⎿  

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

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

⏺ Perfect! I can see that I've reached the end of the response. Now let me
  use a larger line count to capture the complete response in one view:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\u001b[H"})
  ⎿  

⏺ 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 to go to the very beginning of the conversation and capture
  everything systematically:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode","da
                               ta":"\u001b[1;1H"})
  ⎿  

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode","l
                               ines":300})
  ⎿                                          

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

⏺ Now let me clean up by stopping the opencode session:

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

⏺ Based on the captured output, I have successfully completed the task.
  Here is the full summary of what was accomplished:

  TASK_COMPLETE

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

  Full Analysis from GPT OSS 120B Model:

  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, enabling interaction with TUIs,
  debuggers, REPLs, and other interactive programs.

  Architecture:
  1. TerminalManager – core library that creates PTY processes, handles
  I/O, and provides a programmatic API.
  2. TerminalServer – background daemon (Unix-socket) that manages all
  sessions, persists them, and handles client connections.
  3. TerminalClient – used by both the MCP server and CLI to talk to the
  server.
  4. User interfaces – MCP server (for AI agents) and CLI (for humans) both
   use the same client-server protocol.

  Two output modes:
  • Terminal mode (stdout) – full rendered screen (good for TUIs,
  debuggers).
  • Stream mode – raw output, optional ANSI stripping, incremental reads.

  MCP usage (JSON commands):
  • start – launch a process (command, optional cwd and name).
  • stdin – send text or escape-sequences (e.g., \r for Enter, \u001b[A for
   Up).
  • stdout – get rendered screen (optional lines).
  • stream – raw output, optionally since_last and strip_ansi.
  • list, stop, version, kill-server, etc.

  CLI usage (e.g., terminalcp start my-app "npm run dev").

  Programmatic API (via TerminalManager in TypeScript) – start sessions,
  send input, get output, stop sessions, etc.

  Development – npm install, npm run build, npm test.

  License – MIT.

  The model successfully analyzed the project and provided a comprehensive
  explanation of terminalcp's purpose, architecture, and usage modes.

> /cost 
  ⎿ Total cost:            $0.88
    Total duration (API):  2m 18.6s
    Total duration (wall): 2m 24.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  2.3k input, 81 output, 0 cache read, 0 cache 
    write
           claude-sonnet:  170 input, 4.0k output, 2.1m cache read, 53.1k 
    cache write

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