code-assistant-manager
🥈SilverCode Assistant Manager (CAM) lets you manage multiple code assistants like Claude Code, Codex, and Gemini from a single interface. It benefits developers and operations teams by streamlining code generation, review, and debugging workflows. CAM integrates with Python-based tools and workflows, enhancing productivity and reducing context switching.
4690Updated 2mo ago
Intermediate30min to implementautomation
Saves ~45 min per use
Quick InstallView Source
git clone https://github.com/Chat2AnyLLM/code-assistant-manager.gitWorks with:
Claude
Overview
About This Skill
Code Assistant Manager (CAM) is a Go-based CLI tool that unifies management of 17 AI coding assistants including Claude, Codex, Gemini, Copilot, Cursor, Blackbox, Goose, and Continue. It centralizes API keys, configurations, MCP servers, prompts, skills, and plugins in one interface, eliminating the need to switch between multiple tools. CAM solves fragmented workflows by providing consistent system prompts and settings across all assistants through a single terminal command. The tool includes an interactive TUI menu, diagnostic utilities, and access to 381 pre-configured MCP servers. Developers and operations teams use CAM to streamline code generation, review, and debugging while reducing context switching and configuration overhead.
How to Use
Install CAM using the provided install.sh script or pipx, then set up API keys in ~/.env and configuration files in ~/.config/code-agent-manager/. Run 'cam doctor' to verify setup, then use 'cam launch' to open the interactive menu and select your assistant. Manage agents, prompts, skills, and MCP servers using the respective subcommands (cam agent, cam prompt, cam skill, cam mcp).
Use Cases
Manage API keys and configurations for multiple AI assistants from one .env file
Install and synchronize MCP servers across 17 different coding assistants
Create reusable system prompts and custom skills shared across all assistants
Launch any supported AI assistant directly from the CLI without switching contexts
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/Chat2AnyLLM/code-assistant-managerRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
I'm using Code Assistant Manager (CAM) to manage [CODE_ASSISTANT_NAME] for [SPECIFIC_TASK]. Here are the details: [PROJECT_CONTEXT]. Please provide guidance on how to best utilize CAM for this task, including any specific parameters or settings I should configure.
Example Output
# Code Assistant Manager (CAM) Guidance for [SPECIFIC_TASK] ## Recommended Configuration - **Code Assistant**: [CODE_ASSISTANT_NAME] - **Task Type**: [SPECIFIC_TASK] - **Project Context**: [PROJECT_CONTEXT] ## Steps to Utilize CAM Effectively 1. **Initialize CAM**: Start by initializing CAM with the appropriate configuration settings for [CODE_ASSISTANT_NAME]. Ensure that the API keys and endpoints are correctly set up. 2. **Define Task Parameters**: Clearly define the parameters for [SPECIFIC_TASK]. This includes specifying the input data, expected output format, and any constraints or requirements. 3. **Monitor Progress**: Use CAM's monitoring tools to track the progress of the task. This includes checking for errors, monitoring resource usage, and ensuring that the task is completed within the expected timeframe. 4. **Review Output**: Once the task is completed, review the output to ensure that it meets the specified requirements. Use CAM's review tools to identify any issues or areas for improvement. 5. **Optimize Performance**: Based on the review, make any necessary adjustments to the configuration or parameters to optimize performance for future tasks.
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