╭───────────────────────────────────────────────────╮
│ ✻ 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--20250816005845017005-prompt.md and follow the 
  instructions.

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp--202508
      16005845017005-prompt.md)
  ⎿  Read 49 lines (ctrl+r to expand)

⏺ I need to complete the task of starting opencode with the specified
  model, switching to GPT OSS 120B, and capturing the project analysis. Let
   me follow the instructions step by step.

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

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

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

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

⏺ Great! OpenCode has started successfully with the 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"})
  ⎿  

⏺ 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 GPT OSS 120B Groq is
  visible in the recent list. I'll type "GPT OSS 120B" to filter and then
  select it:

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

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

⏺ Great! I can see that the search has filtered to show GPT OSS 120B
  options. I can see "GPT OSS 120B Groq" is highlighted. Now I'll press
  Enter to select it:

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

⏺ terminalcp - terminalcp (MCP)(args:
                               {"action":"stdout","id":"opencode_session"})
  ⎿  … +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 need to
  send the project analysis prompt:

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

⏺ 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 starting to respond. Let me wait a bit more for it
   to complete and then capture the full response:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdout","id":"opencode_ses
                               sion","lines":50})
  ⎿                                          

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

⏺ Great! I can see the response is complete now. Let me capture the full
  response by scrolling up to get all the content:

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

       ┃  # 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_ses
                               sion","lines":200})
  ⎿                                          

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

⏺ I need to scroll up within the conversation area to see the full
  response. Let me try using the stream output to get the complete
  response:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stream","id":"opencode_ses
                               sion","since_last":false,"strip_ansi":true})
  ⎿  4;1m1u q b                     
      b
      b
     … +225 lines (ctrl+r to expand)

⏺ Perfect! I can see the complete response from GPT OSS 120B. Now let me
  clean up by stopping the opencode session:

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

⏺ TASK_COMPLETE

  I successfully completed the task. Here's what happened:

  1. Started opencode with the specified model
  anthropic/claude-sonnet-4-20250514
  2. Successfully switched to GPT OSS 120B using the /models command
  3. Prompted the model to analyze the project with "Read README.md explain
   what this project does"
  4. Captured the complete response from GPT OSS 120B

  Full Summary 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 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, editors)
  - stream – raw incremental output (good for logs, builds)
  • Run multiple processes concurrently without blocking the agent
  • Persist sessions across client disconnects; users can attach to a
  running session from their own terminal (like screen/tmux)

  Key Components:

  | Layer           | Role
                                                                  |
  |-----------------|------------------------------------------------------
  ----------------------------------------------------------------|
  | TerminalManager | Core library that creates PTY processes (via
  node-pty), handles I/O, and provides a programmatic API                 |
  | TerminalServer  | Background daemon (Unix-socket) that holds all
  sessions, persists across restarts                                    |
  | TerminalClient  | Used by the CLI and MCP server to talk to the server
  over the socket                                                 |
  | User Interfaces | MCP Server (exposes a single terminalcp tool) and CLI
   (stand-alone command). Both expose the same JSON-based actions |

  Typical Use-Cases:
  • Debugging – drive LLDB/GDB, Python pdb, etc., step-by-step
  • AI-to-AI collaboration – run other AI tools (Claude, Gemini) as
  subprocesses
  • REPL / shell automation – interact with Python, Node, Ruby REPLs
  • TUI testing – automate Vim, htop, etc., via the TerminalManager API
  • Log/Build monitoring – stream incremental output

  How to Use:
  • MCP – send JSON actions (start, stdin, stdout, stream, stop, etc.) to
  the terminalcp tool
  • CLI – commands like terminalcp start my-app "npm run dev" and
  terminalcp attach my-app

  Installation & Requirements:
  • Node 20+
  • Install via npx @mariozechner/terminalcp@latest (or globally with npm i
   -g @mariozechner/terminalcp)
  • Add the MCP server config to your AI-client (VS Code, Claude, Cursor,
  etc.) using the provided JSON snippet

  Overall Goal:
  Enable AI agents (or any program) to programmatically control any
  interactive terminal program, providing full PTY emulation, persistent
  sessions, and a simple JSON-based protocol for real-time interaction.
  This makes it possible to automate, test, and collaborate with tools that
   normally require a human at the keyboard.

> /cost 
  ⎿ Total cost:            $0.54
    Total duration (API):  1m 12.5s
    Total duration (wall): 1m 14.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  2.5k input, 74 output, 0 cache read, 0 cache 
    write
           claude-sonnet:  150 input, 2.7k output, 1.0m cache read, 50.7k 
    cache write

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