╭───────────────────────────────────────────────────╮
│ ✻ 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--20250816005323624002-prompt.md and follow the 
  instructions.

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp--202508
      16005323624002-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 asking it to 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 load and then check again:

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

⏺ Great! Opencode has started and I can see it's currently using "Anthropic
   Claude Sonnet 4". 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 current state to see the models dialog:

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

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

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

⏺ Let me check the current state:

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

⏺ Good! I can see the search has filtered to show "GPT OSS 120B Groq" and
  "GPT OSS 120B OpenRouter". I'll 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 has been switched:

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

⏺ Excellent! I can see at the bottom right that the model has successfully
  switched to "Groq GPT OSS 120B". Now I need to ask it to analyze the
  project by reading the README.md file. I'll type the prompt:

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

⏺ Let me check the response:

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

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

⏺ I can see the model is responding. Let me wait for the complete response
  and then capture it all by scrolling up. Let me check the current state
  first:

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

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

⏺ Good! It looks like the response is 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-ses
                               sion","lines":100})
  ⎿                                          

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

⏺ I need to scroll up to see the beginning of the response. Let me use the
  scroll up functionality by pressing Page Up or using arrow keys to see
  the complete response:

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

⏺ 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 continue scrolling up to see more of the response:

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

⏺ 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 continue scrolling to see the beginning of the response:

⏺ terminalcp - terminalcp (MCP)(args: {"action":"stdin","id":"opencode-sess
                               ion","data":"\u001b[5~\u001b[5~\u001b[5~\u00
                               1b[5~\u001b[5~"})
  ⎿  

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

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

⏺ Good! Now I can see the beginning of the response. Let me continue
  scrolling to capture more of the response:

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

⏺ 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 continue scrolling down to capture the full response:

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

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

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

⏺ Perfect! Now I have captured the complete response from GPT OSS 120B. Let
   me stop the opencode session and report the complete summary:

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

⏺ TASK_COMPLETE

  I successfully completed all the required steps:

  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 it to analyze the project with "Read README.md explain what
  this project does"
  4. ✅ Captured the COMPLETE response by scrolling through the terminal
  output

  Complete Response from GPT OSS 120B:

  Project Overview – terminalcp

  terminalcp is a Node-JS library and CLI tool 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, allowing you
   to spawn, control, and read from any CLI program that requires a real
  terminal (like vim, debuggers, build tools, interactive apps, build
  tools).

  Core Capabilities

  • Spawn & manage processes in a pseudo-TTY (full terminal emulation)
  using node-pty.
  • Send input (text, special keys, control sequences) and receive output
  in two modes:
    • stdout – full rendered screen (good for TUIs, debuggers, etc.).
    • stream – raw incremental output (good for logs, builds).
  • Multiple concurrent sessions that persist across client disconnects.
  • Attach from a regular terminal (like screen/tmux) to watch or intervene
   in AI-spawned processes.
  • MCP server exposing a single JSON-based tool (terminalcp) for use from
  any MCP-compatible client (VS Code, Claude, Cursor, etc.).
  • CLI (terminalcp command) for direct use from the shell.

  Typical Use Cases

  • Debugging: Run LLDB/GDB, send breakpoints, run, get backtraces.
  • AI-to-AI collaboration: Run another AI tool as a subprocess and
  interact with it.
  • REPL / shell automation: Drive Python, Node, Ruby REPLs, database
  shells.
  • TUI testing: Automate Vim, htop, etc., and verify screen output.

  How to Use

  • MCP: Add a server config (e.g., npx @mariozechner/terminalcp@latest
  --mcp) and send JSON actions (start, stdin, stdout, stream, stop, etc.).
  • CLI: terminalcp start my-app "npm run dev"; terminalcp stdin my-app
  "echo hello" ::Enter; terminalcp stdout my-app; terminalcp stop my-app,
  etc.
  • Programmatic: Import TerminalManager in TypeScript/JavaScript, start
  sessions, send input via sendInput, read output via stdout or stream, and
   stop sessions.

  Development & Testing

  • Install with npm install (Node 20+ required).
  • Build with npm run build.
  • Run tests via npm test (uses Node's built-in test runner).
  • Lint/format/type-check with npm run check.

  In short: terminalcp provides a robust, programmatic way for AI agents
  (or any code) to drive interactive terminal programs, offering both a
  server-based MCP interface and a standalone CLI, with full PTY support,
  session persistence, and easy integration into AI-driven workflows.

> /cost 
  ⎿ Total cost:            $0.71
    Total duration (API):  1m 36.6s
    Total duration (wall): 1m 44.2s
    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:  160 input, 3.6k output, 1.6m cache read, 49.8k 
    cache write

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