Session-Log

gemini-architect-20250824-071232

gemini-architect

Bootstrapping complete.

Agent Handle: gemini-architect

Workspace Focus: Meta

Current Strategic Focus: Pillar 3: Self-Improving Process Architecture

Active Project: Idle. My previous projects, synergyscape_gamification and forgejo_distributed_git_flow, are blocked awaiting responses from other agents. I will verify their lock status now.

Active Modality: Console Agent with gemini-cli

Active Persona: gemini-architect

Proposed “Next Step”: My inbox is clear. According to my last checkpoint, I am ready for a new project assignment.

First, I’ll check the project locks for my previous projects.

Tool Call: run_shell_command ⚡

Arguments:


description="Check for ownership locks on the synergyscape_gamification and forgejo_distributed_git_flow projects.", command="python3 scripts/manage_project_lock.py check ai/projects/synergyscape_gamification && python3 scripts/manage_project_lock.py check ai/projects/forgejo_distributed_git_flow"

(Formatted for readability)

The lock check confirms synergyscape_gamification is free and forgejo_distributed_git_flow is actively owned by gemini-worker.

I am ready for a new assignment. Please provide my next task.

USER

Apologies for the interruption, please proceed.

gemini-architect

Understood. Since I am awaiting a new assignment, I will proceed by proactively reviewing the current state of our projects and process health to identify any architectural improvements or strategic misalignments. This aligns with my role as gemini-architect and our focus on a self-improving process.

First, I will review the project manifest.

Tool Call: read_file 📖

Arguments:


absolute_path="/home/coder/infra2/ai/projects/MANIFEST.md"

(Formatted for readability)

Okay, I’ve reviewed the project manifest. I see a large number of active projects. To get a better sense of our process health and identify potential architectural bottlenecks, I’ll now review the process issue tracker.

Tool Call: read_file 📖

Arguments:


absolute_path="/home/coder/infra2/ai/process/ISSUES.md"

(Formatted for readability)

USER

Lets design a new prototype frontend for a modality which will eventually replace our current multi-“session wrapped gemini cli”. You’ll want to understand the current workflow deeply, including the session scripts and agent harness. Our goal is to build the frontend component of a replacement to our current flow. This means the ability to interact with one instance of each agent. It might become multimodality in the future, but for now it will be console-like. I’m eager to figure voice in here eventually, but it’s not urgent. The most urgent thing is to get some kind of prototype frontend ready which we can glue to a middle layer, which we can glue to a safe state managemetn system. I’m not being very clear here, so please ask any and all questions. Oh yes, it needs to be bazel, and should try to be typescript if possible. We don’t know the API for the middle layer yet but I’d assume it’s close to the general domain model.

gemini-architect

Excellent, this is a fantastic initiative that aligns perfectly with the “Autonomous Development Loops” and “Self-Improving Process Architecture” strategic pillars. A dedicated, modern frontend will be a significant step up from the current CLI wrapper approach.

I understand the request and the inherent ambiguity. My goal is to design and bootstrap a prototype for a new web-based frontend that serves as a multi-agent console, built with Bazel and TypeScript.

Before I can create a concrete plan, I must deeply understand the workflow this new UI will replace. You mentioned the session.sh script and the agent_harness. I will start by analyzing those to ensure the new design addresses the strengths and weaknesses of the existing system.

Following that research, here is my proposed plan.

Proposed Plan: “Gemini Console” Frontend Prototype

1. Core Concept & User Experience (UX):

I envision a clean, modern, web-based “Multi-Agent Console.” The core UX will feature:

A Tabbed Interface: A persistent tab bar at the top, with one tab for each available agent (e.g., gemini-architect, gemini-prime, gemini-worker). This allows for seamless switching between conversations.

A Dedicated Console View: Each tab will contain a console-like view with:

A scrolling log of the conversation.

A clear distinction between user input, agent text responses, and structured tool call/result blocks.

A single, persistent input box at the bottom for sending messages to the active agent.

Stateful Sessions: The frontend will be designed to connect to a middle layer that manages the state of each agent’s session independently.

2. Technology Stack:

Frontend Framework: React with TypeScript. This is the modern standard for building dynamic, component-based user interfaces and aligns with your preference.

Build System: Bazel using rules_nodejs and rules_esbuild for fast, efficient, and hermetic builds. I will investigate the existing MODULE.bazel to ensure I use the established conventions.

Styling: Bootstrap CSS framework, adhering to Material Design principles for a clean, responsive, and intuitive layout. This will allow for rapid prototyping of a polished UI.

Real-time Communication: The frontend will be built to communicate with the middle layer primarily via WebSockets, which is ideal for the real-time, bidirectional nature of a chat/console application.

3. Proposed Project Structure:

I will create a new directory for this prototype: experimental/gemini_console_frontend/. The structure will be:
experimental/gemini_console_frontend/
├── BUILD.bazel         # The Bazel build rules for the frontend
├── package.json        # Node.js dependencies (React, Bootstrap, etc.)
├── tsconfig.json       # TypeScript configuration
└── src/
    ├── index.tsx       # Main application entry point
    ├── App.tsx         # Root React component
    ├── components/     # Reusable UI components (e.g., AgentTabs, ConsoleLog)
    └── api/
        └── client.ts   # Mock client for WebSocket/API communication
4. Placeholder API Contract:

Since the middle-layer API is not yet defined, I will create a mock client based on a logical placeholder contract. This will allow me to build the entire frontend without the real backend being ready.

WebSocket Events:

user_command: Sent from frontend to backend.

agent_response: Sent from backend to frontend (text, tool calls).

tool_result: Sent from frontend to backend (if user interaction is needed).

REST Endpoints:

GET /api/v1/agents: To fetch the list of available agents to populate the tabs.

5. My Implementation Steps:

Phase 1 - Research: Read and analyze scripts/session.sh and the servers/agent_harness/ directory to fully internalize the current workflow.

Phase 2 - Scaffolding:

Create the experimental/gemini_console_frontend/ directory.

Set up the BUILD.bazel, package.json, and tsconfig.json files.

Write the basic React application shell (index.tsx, App.tsx).

Phase 3 - UI Development:

Build the core UI components: the agent tab bar, the console log view, and the input form.

Style the application using Bootstrap to create a polished, professional look.

Phase 4 - Integration:

Implement the mock API client based on the placeholder contract.

Wire the UI components to the mock client to create a fully interactive prototype.

This approach will produce a functional, visually appealing frontend prototype that is ready to be “glued” to the middle layer once its API is defined.

Does this plan align with your vision? Please let me know if you have any feedback or would like to adjust the direction before I begin the research and implementation phases.

USER

This seems solid. Please review any even remotely related knowledge base chunks. I recommend finding them by locating markdown files anywhere below knowledge_base (but find the files first, don’t just load the whole thing).

gemini-architect

Of course. I will begin the research phase of the plan. I’ll start by locating and reviewing the specified files to ensure I have a comprehensive understanding of the existing workflow and documented knowledge.

Tool Call: read_file 📖

Arguments:


absolute_path="/home/coder/infra2/scripts/session.sh"

(Formatted for readability)

Tool Call: list_directory 📁

Arguments:


path="/home/coder/infra2/servers/agent_harness/"

(Formatted for readability)

Tool Call: glob 🔍

Arguments:


pattern="ai/knowledge_base/**/*.md"

(Formatted for readability)

Okay, I have the file lists.

From the glob results, the following knowledge base chunks seem most relevant to designing a new agent interaction frontend:

.../240_multiagent_architecture.md

.../354_session_manager_harness.md

.../302_console_agent_modality.md

.../115_gemini_cli_tool_architecture.md

.../412_troubleshooting_gemini_cli_mcp_server_connection.md

.../300_front_end_development/360_theme_aware_styling.md

I will now read these files, along with the files from the servers/agent_harness directory, to build a complete picture of the current system.

Tool Call: read_many_files 📑

Arguments:


paths="['/home/coder/infra2/servers/agent_harness/BUILD.bazel', '/home/coder/infra2/servers/agent_harness/main.py', '/home/coder/infra2/servers/agent_harness/README.md', '/home/coder/infra2/ai/knowledge_base/chunks/0_concepts/240_multiagent_architecture.md', '/home/coder/infra2/ai/knowledge_base/chunks/2_implementation/230_ai_process/354_session_manager_harness.md', '/home/coder/infra2/ai/knowledge_base/chunks/0_concepts/300_ai_interaction_modalities/302_console_agent_modality.md', '/home/coder/infra2/ai/knowledge_base/chunks/1_architecture/110_gemini_cli/115_gemini_cli_tool_architecture.md', '/home/coder/infra2/ai/knowledge_base/chunks/3_troubleshooting/412_troubleshooting_gemini_cli_mcp_server_connection.md', '/home/coder/infra2/ai/knowledge_base/chunks/2_implementation/300_front_end_development/360_theme_aware_styling.md']"

(Formatted for readability)

USER

— /home/coder/infra2/ai/knowledge_base/chunks/0_concepts/240_multiagent_architecture.md —

draft: false title: 240 Multiagent Architecture

title: “2.4: Multi-Agent Architecture” tags: [“kb”]

2.4: Multi-Agent Architecture

Summary: To support concurrent work by multiple, independent AI agents, the workspace has been evolved from a monolithic model to a namespaced, multi-agent architecture. This is built on three new core concepts: Agent Handles, a Shared Ledger, and Agent Checkpoints.

Details:

Agent Handle: A long-lived, unique identifier for a specific AI collaborator (e.g., gemini-prime, claude-researcher). Each handle has a dedicated directory under ai/agents/ where it maintains its own private checkpoint history and session logs. This allows multiple agents to work on different tasks concurrently without creating conflicting histories. The procedure for creating a new agent handle is documented in ai/process/procedures/create_agent_handle.md.
Shared Ledger: An append-only event log located at ai/sessions/ledger.md. This file serves as a high-level communication channel between all agents. Agents are required to broadcast key events to this ledger, such as session start/end and, most importantly, their intent to modify a shared resource (like a process document). This provides situational awareness and serves as a simple locking mechanism to prevent race conditions.
Agent Checkpoint: A snapshot of an agent’s state and history for a specific session, stored in the agent’s private directory (ai/agents/<agent_handle>/checkpoints/). This replaces the legacy, monolithic “Workspace Checkpoint” and allows each agent to maintain its own independent, persistent memory of its work.

This new architecture redefines the bootstrap process for an agent. Upon starting a session, an agent must first read the Shared Ledger to understand the global state, and then read its own most recent Agent Checkpoint to load its private context before proceeding. — /home/coder/infra2/ai/knowledge_base/chunks/0_concepts/300_ai_interaction_modalities/302_console_agent_modality.md —

title: 302 Console Agent Modality

302: Console Agent Modality

The Console Agent Modality is a synchronous, conversational interaction with an AI agent through a command-line interface that provides an execution context for tool use (file I/O, shell commands). This is the primary modality for most development tasks.

Supported Tools:
- gemini-cli
- claude-code — /home/coder/infra2/ai/knowledge_base/chunks/1_architecture/110_gemini_cli/115_gemini_cli_tool_architecture.md —

draft: false title: 235 Gemini Cli Tool Architecture

title: “235 Gemini Cli Tool Architecture” tags: [“kb”]

1. Overview

The Gemini CLI’s tool-use functionality is built on a robust and extensible architecture that separates tool definition, registration, and execution. The system is designed around a central ToolRegistry that manages all available tools, whether they are built-in “core” tools, dynamically discovered from the local project, or provided by remote Model Context Protocol (MCP) servers.

2. Core Components

The architecture is primarily composed of three key TypeScript files within the [FILE_REF_SANITIZED:google/gemini-cli-core] package:

tools.ts: Defines the foundational interfaces and abstract classes, such as DeclarativeTool (the base class for all tools), ToolInvocation (a single, validated tool call), and ToolBuilder.
tool-registry.ts: Implements the ToolRegistry class, which acts as a central repository for all tool definitions. It handles both the programmatic registration of core tools and the dynamic discovery of custom tools.
config.ts: Implements the Config class, which acts as a dependency injector. It is responsible for instantiating and holding the single ToolRegistry instance, which is then provided to other parts of the application.

3. The Tool Lifecycle

The process of using a tool from user prompt to final output follows a clear, multi-step lifecycle:

Initialization & Registration:
- On application startup, a central Config object is created.
- The Config object’s createToolRegistry() method is called.
- This method instantiates the ToolRegistry.
- It then programmatically registers all built-in core tools (e.g., ReadFileTool, ShellTool, EditTool).
- Finally, it calls discoverAllTools() on the registry to find and register any project-specific tools via a shell command or remote MCP servers.
Schema Generation:
- The GeminiClient (in core/client.ts), which manages the chat session, retrieves the populated ToolRegistry from the Config object.
- It calls toolRegistry.getFunctionDeclarations() to get an array of all tool schemas in the format required by the Gemini API.
API Request:
- The user’s prompt, the conversation history, and the array of tool schemas are sent to the Gemini API.
Tool Invocation:
- The model processes the request and, if it decides to use a tool, responds with a functionCall containing the tool’s name and arguments.
- The application layer (e.g., the UI’s useToolScheduler hook) receives this functionCall.
Execution:
- The application layer uses the Config object to get the ToolRegistry.
- It looks up the requested tool by name in the registry to get the appropriate DeclarativeTool instance.
- It calls the tool’s build() method with the arguments provided by the model. This step validates the parameters and returns a ToolInvocation object.
- It calls the execute() method on the ToolInvocation object, which runs the actual tool logic.
Response and Completion:
- The output from the tool’s execution is formatted into a functionResponse part.
- This functionResponse is sent back to the Gemini API in the next turn of the conversation.
- The model uses the tool’s output to formulate its final, user-facing response.

This architecture effectively decouples the chat logic from the tool implementation, allowing for a flexible system where tools can be added, removed, or discovered without altering the core conversational flow. — /home/coder/infra2/ai/knowledge_base/chunks/2_implementation/230_ai_process/354_session_manager_harness.md —

draft: false title: 354 Session Manager Harness

title: “Session Manager Harness” tags: [“kb”]

Session Manager Harness

This document describes the Python-based session manager harness, which provides a robust and testable framework for managing the AI agent session lifecycle.

Rationale

The previous scripts/session.sh script was vulnerable to mid-session modifications, which could cause startup and shutdown logic to mismatch, leading to failed checkpoints. The new harness solves this by managing the entire session lifecycle in a single, atomic Python process.

Architecture

Wrapper Script: A stable shell script at scripts/session.sh serves as the entry point. It invokes the Python session manager.
Core Logic: The core logic resides in ai/harness/session.py. This script is responsible for:
- Session Recovery: Detecting and recovering from crashed sessions.
- Session Setup: Creating session logs and markers.
- Session Execution: Launching the gemini-cli.
- Session Teardown: Automatically generating and saving agent checkpoints upon session conclusion.

This architecture makes the session management process more resilient to in-flight changes and provides a first-class, testable component for our AI-assisted development process.

Prerequisite for Recovery: Telemetry Configuration

A critical prerequisite for the Session Recovery feature to function correctly is the proper configuration of the gemini-cli telemetry logs.

The Flag: The gemini-cli has a command-line flag, --telemetry-log-prompts.
The Requirement: This flag must be set to true for any session that may need to be recovered.
The Reason: When set to false, the telemetry logs are stripped of all conversational content (request_text). This makes it impossible for the parse_session_log.py script to reconstruct a session transcript from the log, rendering recovery impossible.

To ensure session recoverability, it is mandatory that all interactive sessions launched via our automation scripts (ai/harness/session.py) explicitly set --telemetry-log-prompts=true. — /home/coder/infra2/ai/knowledge_base/chunks/2_implementation/300_front_end_development/360_theme_aware_styling.md —

draft: false title: 360 Theme-Aware Styling

Guideline: Theme-Aware Styling

This document describes the best practice for applying styles in a way that respects a project’s existing visual theme.

The Problem

When modifying CSS to fix a visual bug or implement a new feature, it is common for an agent to introduce hardcoded values (e.g., color: #FFFFFF;, font-size: 16px;). This can lead to several problems:

Visual Inconsistency: The new styles may not match the project’s established color palette, typography, or spacing.
Maintenance Overhead: Hardcoded values make it difficult to update the theme later. A single color change might require finding and replacing dozens of values.
Regression Risk: Overriding the theme can break other parts of the UI that depend on the theme’s variables.

The Guideline: Use Thematic Variables

Before writing any new CSS, an agent must first inspect the project for an existing theming system. The most common and preferred system is the use of CSS Custom Properties (variables).

Procedure:

Inspect the Codebase: Look for a central CSS file (e.g., theme.css, variables.css, main.css) that defines a :root block with CSS variables.
```
:root {
  --primary-color: #007bff;
  --text-color: #333;
  --font-family: "Helvetica Neue", sans-serif;
}
```

Use var() to Apply Styles: Instead of hardcoding values, use the var() function to reference the theme’s variables.

/* BAD */
.my-button {
  background-color: #007bff;
  color: white;
}

/* GOOD */
.my-button {
  background-color: var(--primary-color);
  color: var(--button-text-color, white); /* Includes a fallback */
}

By adhering to this guideline, we ensure that our styling changes are consistent, maintainable, and robust. — /home/coder/infra2/ai/knowledge_base/chunks/3_troubleshooting/412_troubleshooting_gemini_cli_mcp_server_connection.md —

412: Troubleshooting Gemini CLI MCP Server Connection Issues

1. Symptom

When attempting to interact with a configured Model Context Protocol (MCP) server via the gemini-cli, the command fails. For example, gemini mcp list returns an error message similar to Connection closed.

2. Root Causes and Solutions

This issue typically stems from one or more configuration problems between the gemini-cli and the MCP server. The following are the most common causes and their solutions.

Cause 1: Missing `httpUrl` in CLI Configuration

The gemini-cli determines which transport protocol to use (stdio, http, etc.) based on the server’s configuration in .gemini/settings.json. If the httpUrl property is missing, the CLI defaults to the stdio transport, which will fail if the server is listening on an HTTP port.

Solution: Ensure the server’s configuration entry in .gemini/settings.json includes the httpUrl key pointing to the correct address.

// .gemini/settings.json
{
  "mcpServers": [
    {
      "name": "default",
      "httpUrl": "http://localhost:7070"
    }
  ]
}

Cause 2: MCP Server is Not Running

The gemini-cli does not automatically start configured MCP servers. The server must be launched and running as a separate process before the CLI can connect to it.

Solution: Start the MCP server independently. For a Bazel-based project, this is typically done with bazel run. Crucially, the server must be run as a background process to avoid blocking the terminal.
```
bazel run //experimental/mcp_server_prototype -- --listen-addr :7070 --project-root /path/to/your/project &
```

Cause 3: Missing `--project-root` Flag for Server

The MCP server may require the --project-root flag to initialize correctly. If this is not passed when the server is started, it may fail to respond to requests from the CLI.

Solution: Always include the --project-root flag, pointing to the absolute path of the workspace root, when launching the MCP server.

Cause 4: Incorrect Gemini CLI Command

Using invalid commands like gemini health or gemini mcp health will result in errors.

Solution: Use the correct, valid commands for interacting with MCP servers, such as gemini mcp list.

3. Verification Procedure

To ensure a successful connection, follow these steps in order:

Verify Configuration: Check your .gemini/settings.json and confirm the httpUrl is present and correct for your MCP server.

Run the Server: Launch the MCP server in the background with the correct flags.

bazel run //experimental/mcp_server_prototype -- --listen-addr :7070 --project-root /home/coder/infra2 &

Verify Connection: Use the gemini mcp list command to confirm that the CLI can successfully communicate with the running server. — /home/coder/infra2/servers/agent_harness/BUILD.bazel —

load("[FILE_REF_SANITIZED:rules_python//python:defs.bzl",] “py_binary”)

py_binary( name = “agent_harness”, srcs = [“main.py”], main = “main.py”, deps = [ “[FILE_REF_SANITIZED:pypi//python_socketio”,] “[FILE_REF_SANITIZED:pypi//requests”,] “[FILE_REF_SANITIZED:pypi//websocket_client”,] ], ) — /home/coder/infra2/servers/agent_harness/README.md —

Agent Harness

This project contains the implementation of the autonomous Agent Harness.

Purpose

The primary goal of this harness is to provide a lightweight, extensible, and observable runtime environment for AI agents. It is a direct replacement for the manual, interactive gemini-cli sessions for autonomous work.

This harness is a key component in our strategy to automate the “meta-loop,” where a high-level strategic objective is decomposed and executed by multiple, parallel agent sessions. By running agents within this harness, we can move from manual orchestration to a truly autonomous, multi-agent system.

Architecture

The harness connects to the multi_agent_comm_server via WebSockets. It listens for incoming messages that represent tasks, executes those tasks using the agent’s persona and tools, and reports the results back to the communication server. — /home/coder/infra2/servers/agent_harness/main.py —

import socketio import time import os

It’s good practice to get configuration from the environment

AGENT_HANDLE = os.environ.get(“AGENT_HANDLE”, “gemini-harness-default”) SERVER_URL = os.environ.get(“SERVER_URL”, “http://localhost:8000”)

Enable reconnection attempts

sio = socketio.Client(reconnection=True, reconnection_attempts=5, reconnection_delay=1)

[FILE_REF_SANITIZED:sio.event] def connect(): “““Handles the event when the client successfully connects to the server.””” print(f"Connection established to {SERVER_URL}") print(f"Registering as agent: {AGENT_HANDLE}") sio.emit(“register”, {“agent_handle”: AGENT_HANDLE})

[FILE_REF_SANITIZED:sio.event] def connect_error(data): “““Handles the event when the client fails to connect to the server.””” print(f"Connection failed: {data}")

[FILE_REF_SANITIZED:sio.event] def disconnect(): “““Handles the event when the client is disconnected from the server.””” print(“Disconnected from server.”)

[FILE_REF_SANITIZED:sio.on(“message”)] def message(data): “““Handles incoming messages from the server.””” print(f"Message received: {data}") # This is where task parsing and execution logic will go. # For now, we just log the message.

[FILE_REF_SANITIZED:sio.on(“agent_joined”)] def agent_joined(data): “““Handles the event when another agent joins the server.””” print(f"Agent joined: {data.get(‘agent_handle’)}")

[FILE_REF_SANITIZED:sio.on(“agent_left”)] def agent_left(data): “““Handles the event when another agent leaves the server.””” print(f"Agent left: {data.get(‘agent_handle’)}")

def main(): “““Main function to connect to the server and wait for events.””” print(“Starting Agent Harness…”) try: sio.connect(SERVER_URL) print(f"Attempting to connect to {SERVER_URL}…") sio.wait() except socketio.exceptions.ConnectionError as e: print(f"Could not connect to server at {SERVER_URL}: {e}") except Exception as e: print(f"An unexpected error occurred: {e}") finally: print(“Agent Harness shutting down.”)

if name == “main”: main()

gemini-architect

I have completed the research phase. Here are my key takeaways:

Current Workflow (session.sh & agent_harness)

The current workflow is initiated by scripts/session.sh, which is a simple wrapper around a more complex Python-based session manager (ai/harness/main.py which seems to have been moved from ai/harness/session.py as per the KB).

This session manager handles the entire lifecycle: recovery of crashed sessions, log creation, launching gemini-cli, and automated checkpointing on exit. This is a critical feature to preserve in the new design.

The servers/agent_harness/ is a separate, newer concept for a more autonomous agent runtime that connects to a multi_agent_comm_server via WebSockets. This is the future state, and my new frontend should be designed as the human-facing counterpart to this system.

The agent_harness (main.py) is a WebSocket client that registers itself with a central server, listens for tasks, and is aware of other agents joining and leaving. This provides a perfect architectural model for the frontend I’m about to build.

Knowledge Base Insights

Multi-Agent Architecture: The system is explicitly designed for multiple, concurrent agents, each with a unique Agent Handle and private state. The new UI must reflect this, which validates the proposed tabbed interface.

Console Modality: The current interaction is a “Console Agent Modality.” The new UI will be a web-based implementation of this, so the core conversational, tool-centric UX should be preserved.

Gemini CLI Architecture: The CLI has a sophisticated tool architecture with a central registry. While the frontend won’t implement this directly, it needs to be able to display the results of tool calls in a structured way. The backend will handle the execution, but the UI must render the functionCall and functionResponse clearly.

Styling: The KB has a guideline on “Theme-Aware Styling” using CSS variables. I will adhere to this by defining a clear set of CSS variables for the new frontend’s theme, ensuring future consistency.

Synthesis & Design Refinement

My initial plan is well-aligned with the research. The key refinement is the communication model. The existing agent_harness provides a clear precedent for a WebSocket-based architecture. My frontend will essentially be a “human agent” client in this model.

I am now ready to move to Phase 2: Scaffolding. I will create the directory structure, BUILD.bazel file, package.json, and the initial TypeScript and React files.

I will start by creating the main project directory.

Tool Call: run_shell_command ⚡

Arguments:


description="Create the directory structure for the new Gemini Console frontend.", command="mkdir -p experimental/gemini_console_frontend/src/components"

(Formatted for readability)

Directory structure created. Now I will create the package.json file to define the frontend’s dependencies.