╭───────────────────────────────────────────────────╮
│ ✻ 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--20250816001123989000-prompt.md and follow 
  the instructions.

⏺ Read(evaluation-results/claude-code--project-analysis--terminalcp-cli--20
      250816001123989000-prompt.md)
  ⎿  Read 71 lines (ctrl+r to expand)

⏺ I'll complete this task by starting opencode with the specified model,
  switching to GPT OSS 120B, and having it analyze the project.

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

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

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

⏺ Good, opencode is running. Now I'll use the /models command to switch to
  GPT OSS 120B:

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

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

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

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

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

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

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

⏺ Great! I can see the "GPT OSS 120B Groq" option is filtered and visible.
  Now I'll press Enter to select it:

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

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

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

⏺ Excellent! I can see the model has successfully switched to "Groq GPT OSS
   120B" as shown in the bottom right corner. Now I'll ask it to analyze
  the project by reading the README.md:

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

⏺ Bash(npx tsx src/index.ts stdout opencode_session)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    13.2K/10% ($0
    … +43 lines (ctrl+r to expand)


⏺ The model is still processing. I can see it's working on the response
  (showing "ing..." at the bottom). Let me wait a moment and check for the
  complete response:

⏺ Bash(sleep 5)
  ⎿  (No content)

⏺ Bash(npx tsx src/index.ts stdout opencode_session)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    22.9K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Perfect! The response is complete. Now I need to capture the full
  response by scrolling up to see all the output. Let me get the full
  scrollback:

⏺ Bash(npx tsx src/index.ts stdout opencode_session 10000)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    22.9K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ I can see the end of the response, but I need to capture the beginning.
  The output appears truncated. Let me try scrolling up or getting more
  context. Let me try a different approach - let me scroll up in the
  terminal to see the full response:

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

⏺ Bash(npx tsx src/index.ts stdout opencode_session)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    22.9K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Let me continue scrolling up to see more of the response:

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

⏺ Bash(npx tsx src/index.ts stdout opencode_session)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    22.9K/17% ($0
    … +43 lines (ctrl+r to expand)


⏺ Good! I can see the beginning of the response. Now let me continue
  scrolling to capture the complete response. Let me scroll down
  systematically to capture all parts:

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

⏺ Bash(npx tsx src/index.ts stdout opencode_session)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    22.9K/17% ($0
    … +43 lines (ctrl+r to expand)


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

⏺ Bash(npx tsx src/index.ts stdout opencode_session)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    22.9K/17% ($0
    … +43 lines (ctrl+r to expand)


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

⏺ Bash(npx tsx src/index.ts stdout opencode_session)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    22.9K/17% ($0
    … +43 lines (ctrl+r to expand)


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

⏺ Bash(npx tsx src/index.ts stdout opencode_session)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    22.9K/17% ($0
    … +43 lines (ctrl+r to expand)


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

⏺ Bash(npx tsx src/index.ts stdout opencode_session)
  ⎿   ┃  # Explaining project from README.md                               
          ┃
      ┃  /share to create a shareable link                    22.9K/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 report the results:

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

⏺ TASK_COMPLETE

  I successfully completed the task. Here is the complete response from GPT
   OSS 120B analyzing 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 "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) for driving terminals from code
  (e.g., tests, automation).

  Architecture

  1. TerminalManager – core PTY manager (node-pty + xterm.js) handling I/O
  and terminal emulation.
  2. TerminalServer – background process that holds sessions, communicates
  via a Unix socket.
  3. TerminalClient – used by both the MCP server and CLI to talk to the
  server.
  4. User Interfaces – MCP server (for AI agents) and CLI (for humans) both
   use the same underlying client.

  Typical Use Cases

  • AI-driven debugging (LLDB, GDB).
  • Running and monitoring builds.
  • Automated testing of TUIs (vim, htop, etc.).
  • Collaborative AI sessions where multiple users can attach to the same
  terminal.

  In short, terminalcp provides a robust, programmable way for AI agents
  (or any code) to spawn, control, and monitor interactive terminal
  applications, with both programmatic and CLI interfaces.

  ---
  The task was completed successfully:
  1. ✅ Started opencode with anthropic/claude-sonnet-4-20250514 model
  2. ✅ Successfully switched to GPT OSS 120B using /models command
  3. ✅ Prompted the model to analyze the project with "Read README.md
  explain what this project does"
  4. ✅ Captured the complete response by scrolling through the entire
  output
  5. ✅ Reported the full analysis provided by GPT OSS 120B

> /cost 
  ⎿ Total cost:            $0.78
    Total duration (API):  2m 29.9s
    Total duration (wall): 2m 24.2s
    Total code changes:    0 lines added, 0 lines removed
    Usage by model:
        claude-3-5-haiku:  43.7k input, 1.2k output, 0 cache read, 0 cache 
    write
           claude-sonnet:  394 input, 4.2k output, 1.6m cache read, 47.5k 
    cache write

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