Coding Agent

AI coding agent powered by CellCog Co-work. Code generation, debugging, refactoring, codebase exploration, terminal operations — executed directly on your machine. Lightweight with multimedia tools loaded on demand.

Coding Agent — 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):

python

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

All agents except OpenClaw (blocks until done):

python

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",
    chat_tier="max",
    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.

bash

# Claude Code, Cursor, Codex + 70 more agents
npx skills add cellcog/skills --skill cellcog

# OpenClaw
openclaw skills install @cellcog/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):

python

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",
    chat_tier="max",
    enable_cowork=True,
    cowork_working_directory="/Users/me/projects/myapp",
    task_label="auth-refactor",
)

All other agents (blocks until done):

python

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",
    chat_tier="max",
    enable_cowork=True,
    cowork_working_directory="/Users/me/projects/myapp",
    task_label="auth-refactor",
)

Key parameters:

• chat_mode="agent", chat_tier="max" — coding needs the deepest reasoning tier (the SDK applies "max" automatically when enable_cowork=True)
• 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 runs CellCog's agent mode at the max tier with a lean, coding-focused context. 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

---

Choosing Mode & Tier

Use chat_mode="agent", chat_tier="max" for all coding work — code needs the deepest reasoning tier. The SDK applies "max" automatically whenever enable_cowork=True, so co-work sessions get it even without an explicit tier.

"agent core" is a legacy name that still works forever (the server maps it to Agent max), but new code should pass chat_mode="agent", chat_tier="max".

Agent Team (chat_mode="team") is reserved for deep research — use it only when the task IS research that happens to involve code.

---

Example Prompts

New Feature Development

python

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

Bug Fix from Error Log

python

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",
    chat_tier="max",
    enable_cowork=True,
    cowork_working_directory="/Users/me/projects/myapp",
    task_label="fix-userlist-crash",
)

Codebase Refactor

python

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",
    chat_tier="max",
    enable_cowork=True,
    cowork_working_directory="/Users/me/projects/myapp",
    task_label="auth-refactor",
)

Test Generation

python

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",
    chat_tier="max",
    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.

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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

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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.

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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)