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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--20250816002137877004-prompt.md and follow 
  the instructions.

⏺ I'll read the file you specified to understand the instructions.

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp-cli--20
      250816002137877004-prompt.md)
  ⎿  Read 71 lines (ctrl+r to expand)

⏺ I'll follow the instructions to start opencode with the specified model,
  switch to GPT OSS 120B, and have it analyze the project. Let me begin:

⏺ 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
   output:

⏺ Bash(sleep 3)
  ⎿  (No content)

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

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

⏺ Great! Opencode is now running. I can see it's using Anthropic Claude
  Sonnet 4 as shown in the bottom right. 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)

⏺ Perfect! I can see the models dialog is open and I can see "GPT OSS 120B
  Groq" in the recent section. I need to type "GPT OSS 120B" to filter and
  select it. Let me type to filter:

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

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

                ┃   Select Model                                  esc   ┃
     … +39 lines (ctrl+r to expand)

⏺ Great! I can see the filtered list showing "GPT OSS 120B Groq" and "GPT
  OSS 120B OpenRouter". Now I need to press Enter to select the first one
  (GPT OSS 120B Groq):

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

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

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

⏺ Excellent! I can see the model has been successfully switched to "Groq
  GPT OSS 120B" as shown in the bottom right corner. Now I need to prompt
  it to analyze the project by asking it to read the README.md file:

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

⏺ Let me wait a moment for the model to process and respond:

⏺ Bash(sleep 5)
  ⎿  (No content)

  Bash(npx tsx src/index.ts stdout opencode)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ I can see that GPT OSS 120B has provided a response, but I need to
  capture the complete response as requested. Let me scroll up to see the
  full output by getting more lines:

  Bash(npx tsx src/index.ts stdout opencode 100)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ I can see there's more content above. Let me get the full scrollback to
  capture the complete response:

  Bash(npx tsx src/index.ts stdout opencode 1000)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    23.1K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ It seems the response is getting cut off. Let me scroll up to see if
  there's more content by using page up or getting more comprehensive
  output. Let me try pressing Page Up to scroll up in the terminal:

  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                    23.1K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Good! I can see more content now. 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                    23.1K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Perfect! Now I can see the beginning of the response. Let me scroll down
  carefully to capture the complete response by going page by page:

  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                    23.1K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Great! Let me continue scrolling down to get the rest:

  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                    23.1K/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                    23.1K/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                    23.1K/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                    23.1K/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                    23.1K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Perfect! I have successfully captured the complete response from GPT OSS
  120B. Now let me stop the session and clean up:

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

⏺ TASK_COMPLETE

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

  ---
  Read README.md

  terminalcp

  https://github.com/user-attachments/assets/e19a83da-e446-4ccd-9028-9c1cc0
  e09a5e

  Let AI agents control interactive command-line tools like a human would.

  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:

  • Spawn and manage any CLI process (debuggers, REPLs, build tools, etc.)
  in a pseudo-TTY.
  • Send keystrokes, special keys, and control sequences to those
  processes.
  • Read output either as a full rendered screen (for TUIs, debuggers,
  etc.) or as a raw stream (for logs, builds, etc.).
  • Run multiple sessions simultaneously, with each session persisting
  across client disconnects.
  • Allow users to attach to AI-spawned sessions (like screen/tmux) to
  watch or intervene.

  ### Key Features

  • Debugging: drive LLDB, GDB, pdb, etc., step-by-step.
  • AI Collaboration: run other AI tools (Claude, Gemini, etc.) as
  subprocesses.
  • REPL & Shell Interaction: Python, Node, Ruby, database shells, etc.
  • Process Management: start, stop, list, and kill sessions via JSON
  commands (MCP) or a CLI.
  • Two output modes
    • Terminal mode (stdout) – full screen with colors, ideal for TUIs.
    • Stream mode (stream) – raw output, optionally incremental and
  ANSI-stripped.
  • MCP Server: exposes a single terminalcp tool that accepts JSON actions
  (start, stdin, stdout, stream, stop, etc.).
  • CLI: terminalcp start|stop|attach|stdout|stream|... for manual use.
  • Programmatic API: TerminalManager class for programmatic control
  (start, sendInput, stdout, stream, stop).

  ### Architecture

  1. TerminalManager – core PTY manager (node-pty + xterm.js headless)
  handling I/O and terminal emulation.
  2. TerminalServer – background process listening on a Unix socket,
  managing all sessions.
  3. TerminalClient – communication layer used by both MCP server and CLI.
  4. User Interfaces – MCP server (for AI agents) and CLI (for humans) both
   talk to the server.

  ### Typical Use Cases

  • AI-driven debugging (LLDB, GDB).
  • Running and monitoring builds.
  • Automating REPLs or interactive tools.
  • Testing TUI applications.
  • Attaching to AI-spawned processes from a terminal (like tmux/screen).

  ### Getting Started

  1. Install the MCP server (npx @mariozechner/terminalcp@latest --mcp) and
   add it to your MCP client (VS Code, Claude, Cursor, etc.).
  2. Use JSON commands via the MCP tool or the CLI to start processes, send
   input, and retrieve output.
  3. Use the TerminalManager API for programmatic control in Node.js apps.

  In short: terminalcp provides a robust, programmatic way for AI agents
  (and developers) to spawn, control, and monitor interactive terminal
  programs, with full PTY emulation, multi-session support, and both
  programmatic and CLI interfaces.

  ---
  The task was completed successfully:
  ✅ Started opencode with anthropic/claude-sonnet-4-20250514 model
  ✅ Successfully switched to GPT OSS 120B using the /models command✅
  Prompted GPT OSS 120B to analyze the project by reading README.md
  ✅ Captured the complete response from the model
  ✅ Cleaned up the session

> /cost 
  ⎿ Total cost:            $0.88
    Total duration (API):  3m 5.8s
    Total duration (wall): 2m 54.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  47.9k input, 1.4k output, 0 cache read, 0 cache 
    write
           claude-sonnet:  408 input, 4.7k output, 1.9m cache read, 51.5k 
    cache write

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