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stream-chain

Orchestrate multi-agent sequential workflows by passing output context between prompt-based tasks.

Updated Feb 26, 2026
Search Tags:#automation#workflow#refactoring#analysis#multi-agent#pipeline
One-click install
npx skills add https://github.com/dabblersport/webapp --skill stream-chain-dabblersport
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/dabblersport/webapp/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/dabblersport/webapp --skill stream-chain-dabblersport

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the challenge of managing complex, multi-step AI workflows by automating the sequential hand-off of data and context between different agent tasks.

Core Features & Use Cases

  • Custom Chains: Build flexible, multi-step pipelines by chaining arbitrary prompts together.
  • Predefined Pipelines: Utilize battle-tested workflows for common tasks like codebase analysis, refactoring, testing, and performance optimization.
  • Context Persistence: Automatically maintains state across steps, allowing agents to build upon the output of previous operations.

Quick Start

Execute the stream-chain skill to run a custom sequence of prompts for analyzing your codebase structure and generating an improvement plan.

Frequently Asked Questions about stream-chain

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

FAQPage Schema
How do I automate multi-step code analysis and refactoring workflows?▼

Automating multi-step code analysis workflows requires orchestrating multi-agent sequential pipelines that pass output context between successive prompt-based tasks. This allows successive agents to build directly upon previous refactoring and analysis operations.

How does context persistence work in multi-agent sequential pipelines?▼

Context persistence in multi-agent pipelines works by automatically maintaining state across sequential steps. This automated context management passes output data between successive prompt-based tasks without manual intervention.

Can I build custom multi-step automation pipelines for arbitrary prompts?▼

You can build custom multi-step automation pipelines by chaining arbitrary prompts together. This flexible chaining enables complex data transformation and multi-step logic execution tailored to specific workflow requirements.

Does this workflow orchestration approach support performance optimization tasks?▼

This workflow orchestration approach supports performance optimization tasks through standardized pipelines. It provides battle-tested workflows specifically for codebase analysis, refactoring, testing, and performance optimization.

What is the best way to coordinate multiple agents for complex data transformation?▼

The best way to coordinate multiple agents for complex data transformation is using automated agent coordination within sequential workflows. This orchestrates hand-offs between successive prompt-based tasks while managing state automatically.

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