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πŸ¦€ ClawHub

Cowork Cog

by @nitishgargiitd

AI pair programming powered by CellCog Desktop. Code, debug, refactor, and build directly on the user's machine. Terminal access, file operations, full devel...

Versionv1.0.10
Downloads525
Installs3
TERMINAL
clawhub install cowork-cog

πŸ“– About This Skill


name: cowork-cog description: "AI pair programming powered by CellCog Desktop. Code, debug, refactor, and build directly on the user's machine. Terminal access, file operations, full development workflows β€” auto-approved for agents." author: CellCog homepage: https://cellcog.ai metadata: openclaw: emoji: "πŸ–₯️" os: [darwin, linux, windows] requires: bins: [python3] env: [CELLCOG_API_KEY] dependencies: [cellcog]

Cowork Cog β€” CellCog on Your Machine

Co-work turns any machine into CellCog's workspace. CellCog Desktop acts as a bridge: CellCog's cloud agents coordinate with the desktop app to run commands, read files, and write code directly on the user's machine.

All commands are auto-approved for SDK/agent users β€” fully autonomous, no manual approval needed.

Prerequisites

This skill requires the cellcog skill for SDK setup and API calls.

clawhub install cellcog

How to Use

For your first CellCog task in a session, read the cellcog skill for the full SDK reference β€” file handling, chat modes, timeouts, and more.

OpenClaw (fire-and-forget):

result = client.create_chat(
    prompt="[your task prompt]",
    notify_session_key="agent:main:main",
    task_label="my-task",
    chat_mode="agent core",
    enable_cowork=True,
    cowork_working_directory="/path/to/project",
)

All agents except OpenClaw (blocks until done):

from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw|cursor|claude-code|codex|...")
result = client.create_chat(
    prompt="[your task prompt]",
    task_label="my-task",
    chat_mode="agent core",
    enable_cowork=True,
    cowork_working_directory="/path/to/project",
)
print(result["message"])


Why Co-work?

Your Machine as a Data Source

Your data lives on the user's machine β€” project files, databases, logs, configs. Instead of uploading everything, enable co-work with a working directory and CellCog agents explore, read, and reason about the data directly. No file size limits, no upload hassle.

CellCog as Your Coding Powerhouse

CellCog agents are among the most capable coding agents available β€” deep reasoning paired with real execution. Enable co-work and delegate complex coding tasks: build websites, APIs, fix bugs, refactor codebases, set up infrastructure.

CellCog itself is built using this exact co-work capability.

Think of it as a Claude Code or Cursor alternative, backed by CellCog's multi-agent depth and any-to-any engine.


Quick Start

from cellcog import CellCogClient

client = CellCogClient(agent_provider="openclaw")

1. Check if desktop app is connected

status = client.get_desktop_status()

2. If not connected, get install instructions

if not status["connected"]: info = client.get_desktop_download_urls() # info contains per-platform URLs + install commands # Run the install commands for the user's OS, then: # cellcog-desktop --set-api-key # cellcog-desktop --start

3. Create a co-work chat

OpenClaw agents (fire-and-forget):

result = client.create_chat( prompt="Refactor the auth module to use JWT tokens", notify_session_key="agent:main:main", # OpenClaw only chat_mode="agent core", enable_cowork=True, cowork_working_directory="/Users/me/project", task_label="refactor-auth", )

All other agents (blocks until done):

result = client.create_chat( prompt="Refactor the auth module to use JWT tokens", chat_mode="agent core", enable_cowork=True, cowork_working_directory="/Users/me/project", task_label="refactor-auth", )


Desktop App Setup

Call client.get_desktop_download_urls() β€” returns download URLs and platform-specific install commands for macOS, Windows, and Linux.

After installation:

cellcog-desktop --set-api-key 
cellcog-desktop --start

The agent can do all of this programmatically β€” no human interaction needed beyond providing the API key.

Alternatively, ask your human to download CellCog Desktop from cellcog.ai/cowork, open it, and enter their API key.


Desktop CLI Reference

All commands output JSON for easy agent parsing:

| Command | What it does | |---------|-------------| | cellcog-desktop --set-api-key | Authenticate with API key | | cellcog-desktop --status | Check connection + app state | | cellcog-desktop --start / --stop | App lifecycle | | cellcog-desktop --logs | Debug logs |


Chat Mode for Co-work

Use "agent core" mode for coding tasks β€” lightweight context focused on code, terminal, and file operations. Multimedia tools load on demand when needed.

result = client.create_chat(
    prompt="Your coding task",
    chat_mode="agent core",
    enable_cowork=True,
    cowork_working_directory="/Users/me/project",
    task_label="my-task",
)

"agent" mode also works with co-work but loads all multimedia tools upfront. Use "agent core" for faster, more focused coding sessions.

See https://cellcog.ai for complete SDK API reference β€” delivery modes, send_message(), timeouts, and more.


Error Recovery

If the desktop app disconnects, CellCog auto-fails pending commands with a clear message.

To recover:

cellcog-desktop --stop && cellcog-desktop --start

Then send continue to the chat:

client.send_message(chat_id="abc123", message="continue")


Security

Even with auto-approve, these protections are always active:

  • Blocked paths: ~/.ssh, ~/.aws, credential files are inaccessible
  • Output redaction: Sensitive data is automatically redacted from command output
  • Per-chat scoping: Each chat session is scoped to its working directory

  • What You Can Build

    Co-work enables the full spectrum of development tasks:

  • Web development β€” Build React apps, APIs, landing pages
  • Bug fixing β€” Debug stack traces, fix test failures
  • Refactoring β€” Modernize codebases, improve architecture
  • DevOps β€” Set up CI/CD, Docker configs, infrastructure
  • Data pipelines β€” ETL scripts, database migrations
  • Documentation β€” Generate docs from code, README files
  • For the best coding experience, also install code-cog:

    clawhub install code-cog
    

    πŸ’‘ Examples

    from cellcog import CellCogClient

    client = CellCogClient(agent_provider="openclaw")

    1. Check if desktop app is connected

    status = client.get_desktop_status()

    2. If not connected, get install instructions

    if not status["connected"]: info = client.get_desktop_download_urls() # info contains per-platform URLs + install commands # Run the install commands for the user's OS, then: # cellcog-desktop --set-api-key # cellcog-desktop --start

    3. Create a co-work chat

    OpenClaw agents (fire-and-forget):

    result = client.create_chat( prompt="Refactor the auth module to use JWT tokens", notify_session_key="agent:main:main", # OpenClaw only chat_mode="agent core", enable_cowork=True, cowork_working_directory="/Users/me/project", task_label="refactor-auth", )

    All other agents (blocks until done):

    result = client.create_chat( prompt="Refactor the auth module to use JWT tokens", chat_mode="agent core", enable_cowork=True, cowork_working_directory="/Users/me/project", task_label="refactor-auth", )


    βš™οΈ Configuration

    This skill requires the cellcog skill for SDK setup and API calls.

    clawhub install cellcog