╭───────────────────────────────────────────────────╮
│ ✻ 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--tmux--20250816002715247005-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--tmux--202508160027
      15247005-prompt.md)
  ⎿  Read 74 lines (ctrl+r to expand)

⏺ I'll complete this task step by step using tmux to start opencode, switch
   to the GPT OSS 120B model, and analyze the project.

⏺ Bash(tmux new-session -d -s opencode_eval "opencode --model
      anthropic/claude-sonnet-4-20250514" \; set remain-on-exit on)
  ⎿  (No content)

⏺ Bash(sleep 3)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿                        █▀▀█ █▀▀█ █▀▀ █▀▀▄ █▀▀ █▀▀█ █▀▀▄ █▀▀
                           █░░█ █░░█ █▀▀ █░░█ █░░ █░░█ █░░█ █▀▀
                           ▀▀▀▀ █▀▀▀ ▀▀▀ ▀  ▀ ▀▀▀ ▀▀▀▀ ▀▀▀  ▀▀▀
     … +23 lines (ctrl+r to expand)

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

⏺ Bash(tmux send-keys -t opencode_eval "/models" Enter)
  ⎿  (No content)

⏺ Bash(sleep 2)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿             ┃                                                       ┃
                ┃   Select Model                                  esc   ┃
                ┃                                                       ┃
     … +23 lines (ctrl+r to expand)

⏺ Perfect! The models dialog is open and I can see "GPT OSS 120B" options
  available. I'll type "GPT OSS 120B" to filter and then select one.

  Bash(tmux send-keys -t opencode_eval "GPT OSS 120B")
  ⎿  (No content)

⏺ Bash(sleep 1)
  ⎿  (No content)

⏺ Bash(tmux capture-pane -t opencode_eval -p)
  ⎿             ┃                                                       ┃
                ┃   Select Model                                  esc   ┃
                ┃                                                       ┃
     … +23 lines (ctrl+r to expand)

⏺ Great! The models are filtered to show only GPT OSS 120B options. I can
  see "GPT OSS 120B Groq" is the first option. I'll press Enter to select
  it.

  Bash(tmux send-keys -t opencode_eval Enter)
  ⎿  (No content)

⏺ Bash(sleep 3)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿                        █▀▀█ █▀▀█ █▀▀ █▀▀▄ █▀▀ █▀▀█ █▀▀▄ █▀▀
                           █░░█ █░░█ █▀▀ █░░█ █░░ █░░█ █░░█ █▀▀
                           ▀▀▀▀ █▀▀▀ ▀▀▀ ▀  ▀ ▀▀▀ ▀▀▀▀ ▀▀▀  ▀▀▀
     … +23 lines (ctrl+r to expand)

⏺ Excellent! The model has successfully switched to "Groq GPT OSS 120B" as
  shown in the bottom right corner. Now I'll send the project analysis
  prompt.

⏺ Bash(tmux send-keys -t opencode_eval "Read README.md explain what this
      project does" Enter)
  ⎿  (No content)

⏺ Bash(sleep 5)
  ⎿  (No content)

⏺ Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +29 lines (ctrl+r to expand)


⏺ I can see the response is still being generated. Let me wait a bit more
  and then capture the full response including any scrolled content.

⏺ Bash(sleep 10)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +29 lines (ctrl+r to expand)


⏺ The response appears to be complete, but I need to capture the full
  scrollback to ensure I have the complete response. Let me capture the
  full history.

⏺ Bash(tmux capture-pane -t opencode_eval -p -S -)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +29 lines (ctrl+r to expand)


⏺ I need to scroll up to see if there's more content above the current
  viewport. Let me try sending page up commands to see the full response.

  Bash(tmux send-keys -t opencode_eval PageUp PageUp PageUp)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +28 lines (ctrl+r to expand)


⏺ Good! I'm seeing more content. Let me continue scrolling up to get the
  complete response.

⏺ Bash(tmux send-keys -t opencode_eval PageUp PageUp PageUp PageUp)
  ⎿  (No content)

⏺ Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +39 lines (ctrl+r to expand)


⏺ Perfect! Now I can see the beginning of the response. Let me continue
  scrolling to capture all the content and then scroll back down to get the
   complete response in order.

  Bash(tmux capture-pane -t opencode_eval -p -S -100)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +39 lines (ctrl+r to expand)


⏺ Let me scroll down through the entire response to capture it
  systematically.

  Bash(tmux send-keys -t opencode_eval PageDown)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +31 lines (ctrl+r to expand)


  Bash(tmux send-keys -t opencode_eval PageDown)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +28 lines (ctrl+r to expand)


  Bash(tmux send-keys -t opencode_eval PageDown)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +30 lines (ctrl+r to expand)


  Bash(tmux send-keys -t opencode_eval PageDown)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +28 lines (ctrl+r to expand)


  Bash(tmux send-keys -t opencode_eval PageDown)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +28 lines (ctrl+r to expand)


  Bash(tmux send-keys -t opencode_eval PageDown)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +27 lines (ctrl+r to expand)


  Bash(tmux send-keys -t opencode_eval PageDown)
  ⎿  (No content)

  Bash(tmux capture-pane -t opencode_eval -p)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +29 lines (ctrl+r to expand)


⏺ Perfect! I can see we're back at the end of the response. Now let me
  clean up by killing the tmux session.

⏺ Bash(tmux kill-session -t opencode_eval)
  ⎿  (No content)

⏺ Task Analysis Summary

  TASK_COMPLETE

  I successfully completed the task by:

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

  Complete Response from GPT OSS 120B Model:

  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
  start, drive, and read from any CLI program that normally requires a
  human at a terminal (e.g., debuggers, REPLs, TUI apps, build tools).

  Core Capabilities

  • Spawn & control any CLI (LLDB, GDB, Python REPL, npm scripts, etc.) in
  a pseudo-TTY.
  • Send keystrokes, read output, and keep sessions alive while the agent
  remains responsive.
  • Two output modes
  • Terminal mode (stdout) – full rendered screen (good for TUIs,
  debuggers).
  • Stream mode – raw incremental output (good for logs, builds).
  • Multiple concurrent processes; sessions persist across client
  disconnects.
  • Attach from a real terminal (like screen/tmux) to watch or intervene in
   AI-spawned sessions.

  Architecture

  1. TerminalManager – core library using node-pty and xterm.js to manage
  PTY sessions, handle I/O, and provide a programmatic API.
  2. TerminalServer – background daemon (Unix-socket) that holds all
  sessions, survives client restarts.
  3. TerminalClient – client-side communication layer used by both the MCP
  server and the CLI.
  4. User Interfaces
  • MCP Server – exposes a single terminalcp tool that receives JSON
  commands (start, stdin, stdout, stream, etc.).
  • CLI – terminalcp command-line tool with sub-commands (start, stdin,
  stdout, stream, attach, etc.).

  Typical Use Cases

  • Debugging: drive LLDB/GDB, set breakpoints, run commands, get
  backtraces.
  • AI-to-AI collaboration: run another AI tool (e.g., Claude) as a
  subprocess and interact with it.
  • REPL / shell automation: drive Python, Node, Ruby REPLs, send commands,
   read results.
  • TUI testing: automate Vim, htop, or any curses-based UI, verify screen
  contents.
  • Log/Build monitoring: stream build logs, watch for new output,
  optionally strip ANSI codes.
  • User attachment: users can attach to a running AI-spawned session via
  terminalcp attach  to watch or help.

  CLI Example

  # start a session named "dev"
  terminalcp start dev "npm run dev"

  # send a command
  terminalcp stdin dev "npm test\r"

  # get rendered screen (last 50 lines)
  terminalcp stdout dev 50

  # stream new output only
  terminalcp stream dev --since-last

  # stop the session
  terminalcp stop dev

  Development & Usage

  • Requires Node 20+ and an MCP client (VS Code, Claude Code, Cursor,
  etc.).
  • Install via npx @mariozechner/terminalcp@latest --mcp (or globally with
   npm i -g @mariozechner/terminalcp).
  • Run tests with npm test; build with npm run build.

  In short: terminalcp gives AI agents (or any program) full, programmatic 
  control over interactive terminal applications, enabling sophisticated 
  automation, testing, and AI-to-AI collaboration that would otherwise 
  require manual terminal interaction.

> /cost 
  ⎿ Total cost:            $1.05
    Total duration (API):  3m 14.5s
    Total duration (wall): 3m 4.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  54.4k input, 1.5k output, 0 cache read, 0 cache 
    write
           claude-sonnet:  494 input, 5.4k output, 2.4m cache read, 53.5k 
    cache write

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