Hive is an outcome-driven agent development framework that evolves. It benefits operations teams by automating workflows and improving efficiency. It connects to Python-based tools and workflows, enabling self-improving agents that adapt to business needs.
git clone https://github.com/adenhq/hive.githttps://docs.adenhq.com/
Automate the creation of specialized worker agents for sales, marketing, or operations based on natural language goals.
Implement real-time monitoring and observability for agent performance and decision-making processes.
Facilitate continuous evaluation and adaptation of agents to improve their effectiveness over time.
Deploy agents in a headless environment with CI/CD integration for streamlined operations.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/adenhq/hiveCopy 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.
Create an outcome-driven AI agent for [COMPANY] in the [INDUSTRY] sector that can evolve based on [DATA]. Provide a detailed plan outlining the agent's capabilities and expected improvements over time.
### AI Agent Overview **Company:** Tech Innovations Inc. **Industry:** Software Development **Capabilities:** - Automate code reviews and suggest improvements based on previous projects. - Analyze user feedback to adapt features in real-time. - Integrate with existing CI/CD pipelines to enhance deployment processes. ### Expected Improvements - **Month 1:** 75% accuracy in identifying code issues. - **Month 3:** Improved suggestion accuracy to 85% through user feedback analysis. - **Month 6:** Full integration with user workflow, reducing deployment time by 30%. This agent will continuously learn from its interactions, ensuring it remains relevant and effective in optimizing software development processes.
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