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stream-chain

Orchestrate multi-agent workflows through JSON chaining for sequential processing.

2|Updated Apr 30, 2026
Search Tags:#workflow#code analysis#multi-agent#data transformation#sequential processing
One-click install
npx skills add https://github.com/felipeganame/ConsorFix --skill stream-chain-felipeganame
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/felipeganame/ConsorFix/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/felipeganame/ConsorFix --skill stream-chain-felipeganame

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires claude-flow, python, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the orchestration of multi-agent workflows, enabling complex data transformations and sequential processing pipelines without the need for manual coordination.

Core Features & Use Cases

  • Custom Chains: Execute custom prompt sequences with full control for flexible workflows.
  • Predefined Pipelines: Use battle-tested workflows for common tasks like code analysis, refactoring, and testing.
  • Sequential Processing: Ensure each step builds on previous results with full output history.
  • Agent Coordination: Enable natural multi-agent collaboration patterns.
  • Data Transformation: Process and transform data through multiple stages with simplicity.

Quick Start

Use the stream-chain skill to run a custom chain with the prompts "Analyze codebase structure", "Identify improvement areas", and "Generate action plan".

Frequently Asked Questions about stream-chain

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate multi-agent workflows for sequential code analysis?▼

Multi-agent workflow orchestration uses JSON chaining to connect sequential processing pipelines, allowing each step to build on previous results for complex data transformations and code analysis.

What is JSON chaining for data transformation pipelines?▼

JSON chaining is a data flow mechanism that passes structured output between sequential processing stages, ensuring each step in a multi-agent workflow receives the full history of previous results.

Do I need Claude Flow and Python to run custom prompt sequences?▼

Yes, running custom prompt sequences and predefined pipelines requires both Claude Flow and Python libraries to handle the underlying workflow execution and multi-agent coordination.

Can I use predefined pipelines for code refactoring and testing?▼

Yes, predefined pipelines offer battle-tested workflows specifically designed for common development tasks including code refactoring, testing, and codebase structure analysis.

What's the best way to coordinate multiple agents for data processing?▼

Coordinating multiple agents is best achieved through custom chains that execute prompt sequences with full control, enabling natural collaboration patterns and multi-stage data transformation.

How do I run a custom chain to analyze and improve my codebase?▼

To run a custom chain, provide a prompt sequence such as "Analyze codebase structure", "Identify improvement areas", and "Generate action plan" to execute sequential processing with full output history.

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