Agentwise enables multi-agent orchestration for Claude Code, optimizing tokens by 15-30% and improving agent performance through self-improvement and automatic verification. Developers and operations teams benefit from streamlined workflows and reduced costs.
git clone https://github.com/VibeCodingWithPhil/agentwise.gitagentwise is a powerful automation skill designed for Claude Code, enabling multi-agent orchestration that optimizes token usage by 15-30%. This skill features self-improving agents that adapt over time and automatic verification processes to ensure reliability and efficiency in your workflows. By leveraging agentwise, developers and AI practitioners can streamline complex tasks that involve multiple AI agents, enhancing productivity and reducing overhead. The key benefits of agentwise include significant token optimization, which can lead to cost savings in environments where token usage directly impacts performance and expenses. While the exact time savings are currently unknown, the efficiency gained from automating agent interactions and improving their performance can lead to faster project delivery and reduced manual intervention. This skill is particularly valuable for teams focused on AI automation and workflow automation, allowing them to maximize the potential of their AI agents. This skill is ideal for developers, product managers, and AI practitioners who are looking to enhance their AI-first workflows. It is especially relevant for those working in environments where multi-agent systems are commonplace, such as data engineering, machine learning, and software development. Practical use cases include automating data processing tasks, managing complex decision-making processes in real-time, and enhancing the responsiveness of AI-driven applications. Implementation of agentwise is categorized as intermediate, requiring approximately 30 minutes to set up. Users should be familiar with Claude Code and have a basic understanding of multi-agent systems to fully leverage its capabilities. By integrating agentwise into your existing workflows, you can create a more efficient and adaptive AI environment that aligns with modern automation needs.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/VibeCodingWithPhil/agentwiseCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
I need to automate a complex workflow for [COMPANY] in the [INDUSTRY] sector. The process involves [DATA] and requires multiple steps. Can you create a multi-agent orchestration plan using agentwise that optimizes token usage by at least 15% and includes self-improving agents with automatic verification?
## Multi-Agent Orchestration Plan for [COMPANY] ### Agent 1: Data Collection - **Objective**: Gather and preprocess [DATA] from multiple sources - **Token Optimization**: 22% reduction through efficient data chunking - **Self-Improvement**: Automatically refines data extraction patterns - **Verification**: Cross-checks data consistency with source metadata ### Agent 2: Analysis & Insights - **Objective**: Perform trend analysis and generate insights - **Token Optimization**: 18% reduction through optimized prompt structuring - **Self-Improvement**: Adjusts analysis parameters based on initial results - **Verification**: Validates insights against historical data ### Agent 3: Reporting - **Objective**: Generate comprehensive reports and visualizations - **Token Optimization**: 15% reduction through efficient report structuring - **Self-Improvement**: Refines report templates based on user feedback - **Verification**: Ensures all insights are properly cited and sourced **Total Token Optimization**: 18.7% **Estimated Time Savings**: 4.5 hours per week
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