What makes Stream-JSON chaining better than standard chaining?

Stream-JSON allows for more efficient data handling and real-time processing, reducing latency and enabling more complex data transformations between agent steps.

How do I execute a custom multi-step workflow?

You can use the 'run' command followed by your sequence of prompts, which Claude will then execute in order, passing context between each step.

What is the primary purpose of the Stream Chain skill?

Stream Chain is designed to link multiple AI agent tasks together in a sequence, allowing the output of one agent to serve as the direct input for the next.

Are there built-in workflows available in Stream Chain?

Yes, the skill includes a 'pipeline' command that allows you to run predefined, optimized workflows for common tasks like codebase analysis.

Stream Chain

Name: Stream Chain
Author: ricable

byricable

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Productivity & Workflow

Orchestrates multi-agent workflows and sequential data transformations through efficient Stream-JSON processing.

About

Stream Chain is a sophisticated workflow orchestration tool for Claude Code that enables the execution of multi-step pipelines where agent outputs flow seamlessly into subsequent tasks. By utilizing Stream-JSON chaining, it allows developers to build complex automation sequences that coordinate multiple AI interactions for analysis, transformation, and execution. Whether using custom-defined prompt sequences or battle-tested predefined pipelines, this skill provides the necessary structure for advanced agentic workflows that go beyond single-turn interactions.

Key Features

Seamless agent-to-agent output chaining
Predefined pipelines for common development workflows
Custom multi-step prompt sequence execution
Real-time Stream-JSON data processing
1 GitHub stars
Sequential data transformation and refinement

Use Cases

Multi-stage data extraction and transformation pipelines
Complex agentic workflows requiring sequential logic and validation
Automated codebase analysis followed by structural improvement planning

About

Key Features

Seamless agent-to-agent output chaining
Predefined pipelines for common development workflows
Custom multi-step prompt sequence execution
Real-time Stream-JSON data processing
1 GitHub stars
Sequential data transformation and refinement

Use Cases

Multi-stage data extraction and transformation pipelines
Complex agentic workflows requiring sequential logic and validation
Automated codebase analysis followed by structural improvement planning