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DNYoussef
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stream-chain

Chain sequential agent outputs into multi-step workflow pipelines.

4|Updated Oct 31, 2025
Search Tags:#automation#workflow#multi-agent#streaming#orchestration#pipeline#chaining
One-click install
npx skills add https://github.com/DNYoussef/ai-chrome-extension --skill stream-chain-dnyoussef
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/DNYoussef/ai-chrome-extension/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/DNYoussef/ai-chrome-extension --skill stream-chain-dnyoussef

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the manual orchestration of complex, multi-step AI tasks. It automates sequential workflows where each agent's output feeds the next, saving time and ensuring consistent data flow across pipelines for sophisticated transformations and processing.

Core Features & Use Cases

  • Custom Chains: Define and execute bespoke sequences of prompts for maximum flexibility in unique workflows.
  • Predefined Pipelines: Leverage battle-tested workflows for common tasks like analysis, refactoring, testing, and optimization, accelerating development.
  • Context Preservation: Full output from previous steps flows as context to the next, enabling sophisticated data transformations and logical progression.
  • Use Case: Execute a "Security Audit Workflow" where agents sequentially analyze vulnerabilities, categorize issues, propose fixes, and generate security test cases, delivering a comprehensive security report.

Quick Start

Run a custom chain to "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 automate multi-step workflows with chained AI agents?▼

Stream-chain orchestrates sequential workflows by chaining AI agent outputs, where each step's complete result feeds as context to the next. Define custom chains with your own prompts or use predefined pipelines for code analysis, security audits, and data processing, with built-in progress tracking and timing.

What's the difference between custom chains and predefined pipelines in workflow automation?▼

Custom chains let you define bespoke prompt sequences for unique workflows, while predefined pipelines provide battle-tested configurations for common tasks like analysis, refactoring, and testing. Both propagate prior outputs as context and support verbose, timeout, and debug options.

Can I use workflow automation to run security audits across sequential analysis steps?▼

Yes. Stream-chain supports security audit workflows where agents sequentially analyze vulnerabilities, categorize issues, propose fixes, and generate test cases. Each step receives full context from the previous one, delivering a comprehensive security report.

How does context flow between steps in a multi-agent workflow?▼

Context preservation passes the complete output from each step to the next agent, enabling sophisticated data transformations and logical progression. This ensures consistent information flow across your entire pipeline.

What monitoring and debugging options are available during workflow execution?▼

Stream-chain supports verbose mode, timeout controls, and debug options, plus per-step progress reporting, timing metrics, and results tracking so you can monitor each stage of your workflow.

Can I execute a workflow that analyzes codebase structure, identifies improvements, and generates action plans?▼

Yes. Stream-chain supports custom chains for this exact scenario—chain prompts to analyze codebase structure, identify improvement areas, and generate action plans with full context flow between each step.

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