Jules Task Archiver

Chrome Extension for bulk operations on Jules tasks via batchexecute API -- archive tasks and start code suggestions at scale.

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Table of contents

• Features
• Installation
• Usage
• How It Works
• Permissions
• Development
• Related Projects
• Contributing
• License

Features

Archive Tasks

• Bulk archive -- archive all completed tasks for repos with zero open PRs in one click
• GitHub PR check -- skips repos with open PRs to avoid archiving active work
• Force mode -- skip PR check and archive everything

Start Suggestions

• Bulk start -- start all recommended code suggestions (security, performance, testing, cleanup) across repos
• Category-aware prompts -- generates tailored prompts per suggestion category (security fix, performance optimization, test coverage, code cleanup)
• Config capture -- observes Jules UI to capture model config and experiment IDs for accurate reproduction

General

• Multi-account -- processes all Jules tabs (/u/0, /u/1, etc.) automatically
• Dry run mode -- preview what would happen without making changes
• batchexecute API -- direct HTTP calls, no DOM automation, 10x faster than UI clicks
• Live progress -- real-time log and progress bar in popup UI
• State persistence -- operation continues even if popup is closed; progress restored on reopen

Installation

1. Download the latest jules-task-archiver.zip from Releases
2. Extract the zip
3. Open chrome://extensions in Chrome
4. Enable Developer mode (top right toggle)
5. Click Load unpacked and select the extracted folder

Usage

1. Open one or more jules.google.com tabs (supports multiple accounts)
2. Click the extension icon in the toolbar
3. Configure:
   • Operation -- Archive Tasks or Start Suggestions
   • GitHub Owner -- your GitHub username (for PR checks in archive mode)
   • GitHub Token -- optional, for private repos
   • Mode -- Dry Run (preview) or Run (execute)
   • Force -- archive every task regardless of state or open PRs (archive mode only)
   • Scope -- current tab only or all Jules tabs
4. Click Start

Start Suggestions tips

• For best results, manually click "Start" on any suggestion in the Jules UI first -- the extension captures the model config and experiment IDs from that request
• Without this capture, the extension uses sensible defaults that may differ from Jules' current configuration

How It Works

v2 Architecture

popup.js (UI) <-> background.js (batchexecute client) <-> content.js (message relay)
                        |                                        |
                  fetch() to jules.google.com          main-world.js (MAIN world)
                  /_/Swebot/data/batchexecute          reads WIZ_global_data tokens

1. main-world.js runs in the page's MAIN world, reads auth tokens from WIZ_global_data, and observes fetch calls for StartSuggestion config
2. content.js relays tokens and config to the background service worker
3. background.js makes direct HTTP calls to Jules' batchexecute API endpoint
4. popup.js displays real-time progress and manages settings

RPC IDs

RPC	ID	Purpose
ListTasks	p1Takd	Fetch all active tasks
ArchiveTask	Tjmm5c	Archive a single task
ListSuggestions	hQP40d	Fetch code suggestions for a repo
StartSuggestion	Rja83d	Start a suggestion as a new task

Permissions

Permission	Why
storage	Save settings and operation state
tabs	Query all Jules tabs for multi-account support
scripting	Inject content script into pre-existing tabs
jules.google.com	Content script for token extraction
api.github.com	Check open PRs via GitHub REST API

Development

# Lint and format
npx @biomejs/biome check .
npx @biomejs/biome check --write .

# Run tests
node --test

# Load unpacked in chrome://extensions for testing

No build step, no dependencies. Pure vanilla JavaScript with node:test for unit tests.

Related Projects

Check out these MCP servers for AI-powered development:

• wet-mcp -- Web search, extract, and media MCP server
• better-notion-mcp -- Notion API MCP server
• mnemo-mcp -- Persistent AI memory MCP server

Contributing

See CONTRIBUTING.md for guidelines.

License

MIT