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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-cli--20250816001625954002-prompt.md and follow 
  the instructions.

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp-cli--20
      250816001625954002-prompt.md)
  ⎿  Read 71 lines (ctrl+r to expand)

⏺ I'll help you complete this task. I need to start opencode with the
  specified model, switch to GPT OSS 120B, and then ask it to analyze the
  project. Let me start by running opencode with the
  anthropic/claude-sonnet-4-20250514 model.

⏺ Bash(npx tsx src/index.ts start opencode opencode --model
      anthropic/claude-sonnet-4-20250514)
  ⎿  Started session: opencode

⏺ Now let me wait a moment for the session to initialize and then check the
   current state:

⏺ Bash(sleep 3)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿                        █▀▀█ █▀▀█ █▀▀ █▀▀▄ █▀▀ █▀▀█ █▀▀▄ █▀▀

                           █░░█ █░░█ █▀▀ █░░█ █░░ █░░█ █░░█ █▀▀
     … +39 lines (ctrl+r to expand)

⏺ Perfect! The opencode session is running. Now I need to use the /models
  command to switch to GPT OSS 120B:

⏺ Bash(npx tsx src/index.ts stdin opencode "/models" ::Enter)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿             ┃                                                       ┃

                ┃   Select Model                                  esc   ┃
     … +39 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 list. Let me type "GPT OSS 120B" to filter and select
   it:

⏺ Bash(npx tsx src/index.ts stdin opencode "GPT OSS 120B" ::Enter)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿                        █▀▀█ █▀▀█ █▀▀ █▀▀▄ █▀▀ █▀▀█ █▀▀▄ █▀▀

                           █░░█ █░░█ █▀▀ █░░█ █░░ █░░█ █░░█ █▀▀
     … +39 lines (ctrl+r to expand)

⏺ Excellent! I can see that the model has been successfully switched to
  "Groq GPT OSS 120B" (shown in the bottom right corner). Now I need to ask
   it to analyze the project by reading the README.md file:

⏺ Bash(npx tsx src/index.ts stdin opencode "Read README.md explain what
      this project does" ::Enter)
  ⎿  (No content)

⏺ Bash(sleep 5)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode 100)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ I can see some partial response, but I need to capture the complete
  response. Let me get more lines to see the full analysis:

⏺ Bash(npx tsx src/index.ts stdout opencode 1000)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ It looks like the response might be truncated or the model might still be
   processing. Let me scroll up to see if there's more content above by
  getting the full scrollback:

⏺ Bash(npx tsx src/index.ts stdout opencode 10000)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ I'm getting the same truncated output. It seems like the response might
  be incomplete or there might be an issue with how the response is being
  displayed. Let me try sending a follow-up or scrolling up in the
  interface to see if there's more content:

⏺ Bash(npx tsx src/index.ts stdin opencode ::PageUp ::PageUp ::PageUp)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Good! Now I'm seeing more of the response. Let me continue scrolling up
  to capture the complete response:

⏺ Bash(npx tsx src/index.ts stdin opencode ::PageUp ::PageUp ::PageUp
      ::PageUp ::PageUp)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Great! Now I can see the beginning of the response. Let me continue
  scrolling down methodically to capture the complete response. Let me use
  Page Down to navigate through the entire response:

⏺ Bash(npx tsx src/index.ts stdin opencode ::PageDown)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Excellent! Now I can see more of the analysis. Let me continue scrolling
  to get the complete response:

⏺ Bash(npx tsx src/index.ts stdin opencode ::PageDown)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Bash(npx tsx src/index.ts stdin opencode ::PageDown)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Bash(npx tsx src/index.ts stdin opencode ::PageDown)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Bash(npx tsx src/index.ts stdin opencode ::PageDown)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Perfect! Now I can see we're getting to the end. Let me get the final
  part:

⏺ Bash(npx tsx src/index.ts stdin opencode ::PageDown)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                      23K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Excellent! I have successfully captured the complete response from GPT
  OSS 120B. Now let me stop the opencode session as requested:

⏺ Bash(npx tsx src/index.ts stop opencode)
  ⎿  stopped opencode

⏺ Complete Response from GPT OSS 120B

  I have successfully completed the task. Here is the COMPLETE response
  provided by GPT OSS 120B when asked to analyze the project:

  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 play-wright-style interface for terminal programs, allowing
   them to be started, fed input, and have their output read in real-time.

  Core Capabilities

  • Spawn & control any CLI (debuggers, REPLs, build tools, etc.) in a
  pseudo-TTY.
  • Two output modes
  • Terminal mode (stdout) – full rendered screen (useful for TUIs,
  debuggers).
  • Stream mode – raw incremental output (good for logs, builds).
  • Send keystrokes (text, arrows, Ctrl-C, etc.) via escape-sequence
  strings.
  • Run multiple processes concurrently without blocking the agent.
  • Attach from a real terminal (like screen/tmux) to watch or intervene in
   AI-spawned sessions.

  Architecture

  1. TerminalManager – core library that creates PTY processes (via
  node-pty), emulates a terminal (using xterm.js headless), and handles
  I/O.
  2. TerminalServer – a persistent background process that holds all
  sessions and listens on a Unix-domain socket.
  3. TerminalClient – used by both the CLI and the MCP server to talk to
  the server.
  4. User Interfaces
  • MCP Server – exposes a single terminalcp tool that receives JSON
  commands (start, stdin, stdout, stream, stop, etc.).
  • CLI – a command-line wrapper (terminalcp start …, terminalcp attach …,
  etc.) that uses the same client.

  Typical Use-Cases

  • Debugging: drive LLDB/GDB, Python pdb, etc., step-by-step.
  • AI-to-AI collaboration: run Claude, Gemini, etc., as subprocesses.
  • REPL / shell automation: interact with Python, Node, Ruby REPLs.
  • TUI testing: automate Vim, htop, or any curses-based UI.
  • Log/Build monitoring: stream build output, watch logs, get only new
  output.
  • User attachment: users can attach to AI-spawned sessions via terminalcp
   attach .

  How to Use

  • MCP: send JSON actions (start, stdin, stdout, stream, stop, etc.) to
  the server.
  • CLI: terminalcp start my-app "npm run dev"; terminalcp stdin my-app
  "echo hello" ::Enter; terminalcp stdout my-app; terminalcp attach my-app,
   etc.

  Development

  • Install with Node 20+ (npm install), run tests (npm test), or use the
  CLI directly (npx tsx src/index.ts …).

  In short: terminalcp provides a programmable, persistent 
  terminal-emulation layer that lets AI agents (or any code) spawn, 
  control, and monitor interactive CLI tools as if a human were typing, 
  with both a programmatic API and a convenient CLI.

  TASK_COMPLETE

> /cost 
  ⎿ Total cost:            $0.81
    Total duration (API):  2m 42.4s
    Total duration (wall): 2m 29.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  45.7k input, 1.3k output, 0 cache read, 0 cache 
    write
           claude-sonnet:  398 input, 4.3k output, 1.7m cache read, 48.7k 
    cache write

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