stream-chain

Stream-Chain provides a robust framework for executing multi-step AI agent workflows where the output of one process serves as the input for the next. Designed for the Ruflo orchestration ecosystem, this skill enables developers to create sophisticated sequential pipelines that handle complex software engineering tasks. It is ideal for orchestrating specialized agents that need to perform layered operations such as codebase structure analysis, automated refactoring, security auditing, and test case generation. By facilitating structured data flow between agents, it ensures that context is maintained and evolved throughout the execution chain.

• Dual-mode execution supports both custom chains for flexible prompt sequencing and predefined, battle-tested pipelines for common development lifecycle tasks.

• Seamless context propagation ensures each agent step is fully aware of prior transformations, enabling iterative development patterns like TDD or systematic refactoring.

• Native support for timeout management, verbose logging, and debug modes to facilitate monitoring and troubleshooting in complex multi-step routines.

• Extensible architecture allows for defining custom, reusable pipelines within the .claude-flow/config.json configuration, standardizing workflows across different project contexts.

• Utilize the stream-chain run command for bespoke sequences, providing multiple prompts that flow sequentially through the agent swarm.

• Leverage predefined pipeline commands like analysis, refactor, test, and optimize to automate standard technical debt reduction or quality assurance tasks.

• Ensure input prompts are granular to leverage the benefits of step-wise chain execution, allowing for better error isolation and modular task completion.

• Keep track of total execution time and progress reports provided by the internal orchestrator, especially when utilizing long-running chains or deep analysis pipelines.

• Be mindful of timeout constraints for intensive operations, configuring the --timeout option appropriately to prevent premature termination of complex multi-agent reasoning chains.