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cowork-cog协同工作齿轮

Agent Skill

cowork-cog 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

7,294

周安装

307

GitHub Stars

公开资料未说明

下载量

2,554
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:cowork-cog(协同工作齿轮)
来源仓库:https://github.com/nitishgargiitd/cowork-cog
安装命令:
openclaw skills install cowork-cog
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install cowork-cog

简介

cowork-cog 用于辅助前端页面、组件和交互逻辑开发,适合维护前端项目或生成界面代码。

  • 基于 CellCog Desktop 提供本地编码、调试和构建支持,覆盖终端和文件操作。
  • 通过 clawhub 安装,需确认是否会执行命令或修改项目文件,并评估安全风险。
  • 建议在受控环境中使用,避免在生产环境直接运行未知脚本。
  • 适用于开发协作,但不承诺稳定性,需人工复核关键变更。

SKILL.md

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 — auto-approved for agents.
author
CellCog
homepage
https://cellcog.ai
metadata
openclaw
emoji
🖥️
os
[darwin, linux, windows]
requires
bins
[python3]
env
[CELLCOG_API_KEY]
dependencies
[cellcog]

Cowork Cog — 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 — 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 — 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 — 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 — 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_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() — returns download URLs and platform-specific install commands for macOS, Windows, and Linux.

After installation:

cellcog-desktop --set-api-key <CELLCOG_API_KEY>
cellcog-desktop --start

The agent can do all of this programmatically — 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:

CommandWhat it does
cellcog-desktop --set-api-key <key>Authenticate with API key
cellcog-desktop --statusCheck connection + app state
cellcog-desktop --start / --stopApp lifecycle
cellcog-desktop --logsDebug logs

Chat Mode for Co-work

Use "agent core" mode for coding tasks — 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 — 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 — Build React apps, APIs, landing pages
  • Bug fixing — Debug stack traces, fix test failures
  • Refactoring — Modernize codebases, improve architecture
  • DevOps — Set up CI/CD, Docker configs, infrastructure
  • Data pipelines — ETL scripts, database migrations
  • Documentation — Generate docs from code, README files

For the best coding experience, also install code-cog:

clawhub install code-cog

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

81.85%
按下载量换算2,090

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

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权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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