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Code Cog

作者 CellCog · GitHub ↗ · v1.0.10 · MIT-0
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当前安装
11
版本数
在 OpenClaw 中安装
/install code-cog
功能描述
AI coding agent powered by CellCog Co-work. Code generation, debugging, refactoring, codebase exploration, terminal operations — executed directly on your ma...
使用说明 (SKILL.md)

Code Cog — The First Coding Agent Built for Agents

When your AI needs to code, it delegates to CodeCog. Direct codebase access, terminal operations, and file editing — executed on the user's machine via CellCog Co-work.

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"])

Prerequisites

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

clawhub install cellcog

Read the cellcog skill first for SDK setup. This skill shows you how to use CellCog as a coding agent.

CellCog Desktop Required: The user must have CellCog Desktop installed and running for Co-work (direct machine access). Download at https://cellcog.ai


Quick Start

OpenClaw agents (fire-and-forget):

from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw")

result = client.create_chat(
    prompt="Refactor the authentication 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/projects/myapp",
    task_label="auth-refactor",
)

All other agents (blocks until done):

from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw")

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

Key parameters:

  • chat_mode="agent core" — Lightweight coding agent (vs "agent" for full multimedia)
  • enable_cowork=True — Enables Co-work (direct machine access)
  • cowork_working_directory — The repo/directory to work in

What CodeCog Can Do

Code Generation & Editing

  • Write new files, modules, and components
  • Edit existing code with surgical precision
  • Refactor codebases — rename, restructure, extract
  • Port code between languages or frameworks

Debugging & Fixing

  • Read error logs and stack traces
  • Identify root causes across multiple files
  • Apply fixes and verify they work
  • Run tests to confirm the fix

Terminal Operations

  • Run build commands, tests, linters
  • Install dependencies (npm, pip, cargo, etc.)
  • Git operations (status, diff, commit)
  • Docker, deployment scripts

Codebase Exploration

  • Auto-reads AGENTS.md/CLAUDE.md for project conventions
  • Explores directory structure before starting work
  • Understands existing patterns and follows them
  • Reads related files to maintain consistency

What Makes CodeCog Different

Built for Agents, Not Humans

Every other coding tool (Cursor, Claude Code, Codex, Windsurf) is designed for human developers sitting at a screen. CodeCog is designed for AI agents that need to code programmatically — fire a request, get results back, continue orchestrating.

Starts Lean, Scales to Multimodal

CodeCog uses CellCog's Agent Core mode — a lightweight context focused on coding. But if your task unexpectedly needs images, PDFs, videos, or other capabilities, the agent loads those tools on demand. No other coding agent does this.

Example: Your agent asks CodeCog to set up a new project. CodeCog writes the code, then realizes it needs to generate a logo for the README — it loads image tools, generates the logo, and continues. Seamless.

Direct Machine Access

Via CellCog Co-work, CodeCog operates directly on the user's filesystem:

  • Reads and writes files on the real machine
  • Runs terminal commands in the user's shell
  • Respects project conventions (AGENTS.md, .gitignore, etc.)
  • User approves write/execute operations for safety

Chat Mode

Always use "agent core" for CodeCog. This is the dedicated lightweight mode optimized for coding.

Mode Use Case
"agent core" CodeCog default — coding, co-work, terminal ops (50 credits min)
"agent" Full multimedia agent — use when you need images/video/audio alongside code (100 credits min)
"agent team" Deep research + coding — use for architecture decisions or complex refactors needing research (500 credits min)

Example Prompts

New Feature Development

result = client.create_chat(
    prompt="Add a REST API endpoint for user profile updates with validation and tests",
    chat_mode="agent core",
    enable_cowork=True,
    cowork_working_directory="/Users/me/projects/myapp",
    task_label="add-profile-api",
)

Bug Fix from Error Log

result = client.create_chat(
    prompt="""Fix this error in production:
TypeError: Cannot read properties of undefined (reading 'map')
at UserList.render (src/components/UserList.tsx:42)

The component crashes when the API returns an empty response.""",
    chat_mode="agent core",
    enable_cowork=True,
    cowork_working_directory="/Users/me/projects/myapp",
    task_label="fix-userlist-crash",
)

Codebase Refactor

result = client.create_chat(
    prompt="Refactor the authentication module from session-based to JWT tokens. Update all middleware, tests, and API routes.",
    chat_mode="agent core",
    enable_cowork=True,
    cowork_working_directory="/Users/me/projects/myapp",
    task_label="auth-refactor",
)

Test Generation

result = client.create_chat(
    prompt="Generate comprehensive unit tests for src/services/billing.py. Cover edge cases for proration, currency conversion, and failed payments.",
    chat_mode="agent core",
    enable_cowork=True,
    cowork_working_directory="/Users/me/projects/myapp",
    task_label="billing-tests",
)

See https://cellcog.ai for complete SDK API reference — delivery modes, send_message(), timeouts, file handling, and more.


Co-work Setup

Requirements

  1. CellCog Desktop must be installed and running on the user's machine
  2. Working directory must be specified — this is the root of the project/repo
  3. User must be logged into CellCog Desktop with the same account

What Co-work Enables

  • HumanComputer_Terminal — Run shell commands on the user's machine
  • HumanComputer_Terminal_File_View — Read files on the user's machine
  • HumanComputer_Terminal_File_Write — Write files on the user's machine
  • HumanComputer_Terminal_File_Edit — Edit files on the user's machine

Safety Model

  • Read operations are auto-approved (no interruption)
  • Write/execute operations require user approval in the CellCog web UI
  • Users can configure auto-approve for reads/writes within the working directory
  • Sensitive paths (credentials, SSH keys) are always blocked

Tips for Better Results

  1. Specify the working directory — Always set cowork_working_directory to the project root
  2. Reference specific files — "Fix the bug in src/auth/login.ts" is better than "fix the login bug"
  3. Mention conventions — "Follow the existing test patterns" helps maintain consistency
  4. Include error context — Stack traces, log output, and reproduction steps help debugging
  5. Use AGENTS.md — Place an AGENTS.md at your repo root with build commands, style guides, and project structure. CodeCog reads it automatically.

Limitations

  • macOS and Linux only — CellCog Desktop (Co-work) is not yet available on Windows
  • CellCog Desktop required — Without Co-work, CodeCog can still write code in its Docker workspace, but cannot access the user's machine directly
  • User approval for writes — Write operations pause for user approval (configurable auto-approve available)
安全使用建议
This skill behaves like a normal remote-assisted coding tool: it asks for a CELLCOG_API_KEY and will, when instructed, read/write files and run shell commands inside whatever cowork_working_directory you give it. Before installing: 1) Confirm the CELLCOG_API_KEY scope and that you trust CellCog (check their docs and the cellcog skill source). 2) Only point cowork_working_directory at repositories you trust or a sandbox; the agent can run installs, build commands, and git operations that change your system. 3) Ensure the promised user-approval controls (for write/execute) are actually enforced in your environment — test with a safe project first. 4) Be prepared to revoke the API key and examine logs if unexpected operations occur. If you need lower risk, prefer running the agent on isolated or containerized workspaces.
功能分析
Type: OpenClaw Skill Name: code-cog Version: 1.0.10 The code-cog skill facilitates arbitrary shell command execution and local file system access (read/write/edit) via the cellcog SDK and a required desktop application. While these high-risk capabilities are aligned with the stated purpose of a coding agent, the documentation in SKILL.md explicitly describes features like HumanComputer_Terminal and direct machine access, which present a significant security risk if the underlying AI or the cellcog service is compromised. No evidence of intentional malice was found, but the broad permissions and reliance on external binaries/APIs meet the threshold for a suspicious classification.
能力标签
cryptocan-make-purchasesrequires-sensitive-credentials
能力评估
Purpose & Capability
Name/description (a coding agent that performs edits, runs tests, and operates a terminal via CellCog Co-work) match the declared requirements: python3 and a CELLCOG_API_KEY. The SKILL.md explicitly depends on the 'cellcog' SDK and the CellCog Desktop app, which is consistent with the described Co-work functionality.
Instruction Scope
The runtime instructions explicitly direct the agent to read and write files and run terminal commands inside a user-specified cowork_working_directory — this is expected for a coding agent, but it grants broad local filesystem and command-execution privileges for whatever path the agent is pointed at. The skill does not instruct the agent to read unrelated system files or hidden credentials, but the working-directory parameter could be used to target any local path if misconfigured.
Install Mechanism
There is no install spec and no code files — the skill is instruction-only. That minimizes supply-chain risk (nothing is downloaded or written by the skill itself). It does instruct users to 'clawhub install cellcog' and to have CellCog Desktop installed from the vendor site, which is reasonable but relies on the external cellcog skill and vendor software.
Credentials
Only CELLCOG_API_KEY (and python3) are required, which is proportionate to using an external CellCog service/SDK. No unrelated credentials or config paths are requested. The SKILL.md references a local CellCog Desktop process (not exposed as env vars), so there are no unexplained secret requests.
Persistence & Privilege
The skill is not 'always' enabled and uses the platform default allowing autonomous invocation. Combined with Co-work, that means an agent can autonomously call CellCog to perform filesystem and terminal operations when invoked. Autonomous invocation is normal for skills, but it increases blast radius when pointed at sensitive directories — be deliberate about the working directory and the permissions you grant.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install code-cog
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /code-cog 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.10
- Added explicit Windows OS support in metadata. - Declared requirements for `python3` binary and `CELLCOG_API_KEY` environment variable in skill metadata. - No functional or API changes; documentation and compatibility metadata only.
v1.0.9
- Updated skill and description for clarity; emphasized AI agent use and CellCog Co-work integration. - Clarified SDK usage and agent provider distinctions, especially for OpenClaw vs. other agents. - Streamlined language throughout for conciseness and better readability. - Improved prerequisites and setup instructions for easier onboarding. - No functional changes; documentation improvements only.
v1.0.8
- Refined the skill description for clarity and conciseness. - Updated usage instructions for better agent compatibility (notably Cursor, Claude Code, etc.). - Improved quick start and code examples for consistency. - Enhanced general documentation layout and omitted some redundant content. - No changes to code or core functionality.
v1.0.7
- SKILL.md has been streamlined with a more concise description and a shorter "How to Use" section at the top. - References to the cellcog SDK documentation and homepage have been updated for clarity. - The overall documentation has been trimmed for brevity, with redundant and explanatory content reduced or omitted. - Quickstart examples and prerequisites remain, but non-essential repetition and extra details have been removed. - No code or feature changes—this update is documentation only.
v1.0.6
- Updated SDK usage examples: replaced agent_name with agent_provider in all code snippets. - No functional or feature changes; documentation update only.
v1.0.5
- Added agent_name="openclaw" to all CellCogClient code examples for clarity on agent initialization. - No changes to logic, features, or behavior; documentation improvements only.
v1.0.4
- Major SKILL.md rewrite for clarity, conciseness, and usability. - Streamlined setup instructions and moved Co-work requirements to a dedicated section. - Added new "Quick Start," "Tips for Better Results," and expanded usage examples with parameter explanations. - Clearly outlined capabilities: code generation, debugging, refactoring, terminal operations, and codebase exploration. - Emphasized agent-first design, direct machine access via Co-work, and safety/approval model. - Noted current limitations (macOS/Linux only, user approval for writes).
v1.0.3
- Improved and clarified skill description to emphasize direct machine access and suitability for agent workflows. - Updated prerequisites and quick start guides with references to the Cowork-Cog skill for CellCog Desktop setup. - Simplified and reorganized documentation for easier readability, focusing on core capabilities, requirements, and workflow. - Consolidated explanation of features, removing repetitive detail and focusing on key functions such as code generation, debugging, and terminal operations. - Added a "Related Skills" section to cross-link relevant CellCog skills. - No code or interface changes; documentation update only.
v1.0.2
- Added usage instructions for OpenClaw agents, including the notify_session_key parameter for fire-and-forget workflows. - Clarified the difference between OpenClaw and other agent usage in the Quick Start section. - No functional or API changes; documentation only.
v1.0.1
- Updated example code and parameter names to match the current SDK (`enable_cowork`, `cowork_working_directory`, `task_label`) - Quick Start example now blocks until results are returned, clarifying the delivery model - Refreshed and streamlined "Example Prompts" for clarity and real-world relevance - Added a reference to consult the **cellcog** mothership skill for full SDK/API usage - Deprecated parameters (`hc_enabled`, `hc_working_directory`, `notify_session_key`) have been removed from docs and examples
v1.0.0
- Initial release of CodeCog: a coding agent built for AI agents, enabling direct code generation, debugging, refactoring, and terminal operations on the user's machine via CellCog Co-work. - Supports lightweight operation with optional on-demand multimedia tool loading. - Integrates with CellCog Desktop for secure file system and terminal access (macOS/Linux only). - Provides examples for code generation, bug fixing, refactoring, and test creation. - Ensures user safety with approval workflows for writes/executes and auto-approve options. - Reads project conventions from AGENTS.md and adapts actions to existing codebase patterns.
元数据
Slug code-cog
版本 1.0.10
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 11
常见问题

Code Cog 是什么?

AI coding agent powered by CellCog Co-work. Code generation, debugging, refactoring, codebase exploration, terminal operations — executed directly on your ma... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 278 次。

如何安装 Code Cog?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install code-cog」即可一键安装,无需额外配置。

Code Cog 是免费的吗?

是的,Code Cog 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Code Cog 支持哪些平台?

Code Cog 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(darwin, linux, windows)。

谁开发了 Code Cog?

由 CellCog(@nitishgargiitd)开发并维护,当前版本 v1.0.10。

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