stream-chain

name	stream-chain
description	Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows. Use when piping output between agents/tools as streaming JSON or building a sequential transform pipeline.
version	1.0.0
category	workflow
tags	["streaming","pipeline","chaining","multi-agent","workflow"]

Stream-Chain Skill

Execute sophisticated multi-step workflows where each agent's output flows into the next, enabling complex data transformations and sequential processing pipelines.

Overview

Stream-Chain provides two powerful modes for orchestrating multi-agent workflows:

1. Custom Chains (run): Execute custom prompt sequences with full control
2. Predefined Pipelines (pipeline): Use battle-tested workflows for common tasks

Each step in a chain receives the complete output from the previous step, enabling sophisticated multi-agent coordination through streaming data flow.

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Quick Start

Run a Custom Chain

claude-flow stream-chain run \
  "Analyze codebase structure" \
  "Identify improvement areas" \
  "Generate action plan"

Execute a Pipeline

claude-flow stream-chain pipeline analysis

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Reference

The full detail lives in references/ and loads only when needed:

• references/pipelines.md — custom chains, predefined pipelines, custom pipeline definitions & advanced use cases.

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Best Practices

1. Clear and Specific Prompts

Good:

"Analyze authentication.js for SQL injection vulnerabilities"

Avoid:

"Check security"

2. Logical Progression

Order prompts to build on previous outputs:

1. "Identify the problem"
2. "Analyze root causes"
3. "Design solution"
4. "Implement solution"
5. "Verify implementation"

3. Appropriate Timeouts

• Simple tasks: 30 seconds (default)
• Analysis tasks: 45-60 seconds
• Implementation tasks: 60-90 seconds
• Complex workflows: 90-120 seconds

4. Verification Steps

Include validation in your chains:

claude-flow stream-chain run \
  "Implement feature X" \
  "Write tests for feature X" \
  "Verify tests pass and cover edge cases"

5. Iterative Refinement

Use chains for iterative improvement:

claude-flow stream-chain run \
  "Generate initial implementation" \
  "Review and identify issues" \
  "Refine based on issues found" \
  "Final quality check"

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Integration with Claude Flow

Combine with Swarm Coordination

# Initialize swarm for coordination
claude-flow swarm init --topology mesh

# Execute stream chain with swarm agents
claude-flow stream-chain run \
  "Agent 1: Research task" \
  "Agent 2: Implement solution" \
  "Agent 3: Test implementation" \
  "Agent 4: Review and refine"

Memory Integration

Stream chains automatically store context in memory for cross-session persistence:

# Execute chain with memory
claude-flow stream-chain run \
  "Analyze requirements" \
  "Design architecture" \
  --verbose

# Results stored in .claude-flow/memory/stream-chain/

Neural Pattern Training

Successful chains train neural patterns for improved performance:

# Enable neural training
claude-flow stream-chain pipeline optimize --debug

# Patterns learned and stored for future optimizations

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Troubleshooting

Chain Timeout

If steps timeout, increase timeout value:

claude-flow stream-chain run "complex task" --timeout 120

Context Loss

If context not flowing properly, use --debug:

claude-flow stream-chain run "step 1" "step 2" --debug

Pipeline Not Found

Verify pipeline name and custom definitions:

# Check available pipelines
cat .claude-flow/config.json | grep -A 10 "streamChain"

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Performance Characteristics

• Throughput: 2-5 steps per minute (varies by complexity)
• Context Size: Up to 100K tokens per step
• Memory Usage: ~50MB per active chain
• Concurrency: Supports parallel chain execution

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Related Skills

• SPARC Methodology: Systematic development workflow
• Swarm Coordination: Multi-agent orchestration
• Memory Management: Persistent context storage
• Neural Patterns: Adaptive learning

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Examples Repository

Complete Development Workflow

# Full feature development chain
claude-flow stream-chain run \
  "Analyze requirements for user profile feature" \
  "Design database schema and API endpoints" \
  "Implement backend with validation" \
  "Create frontend components" \
  "Write comprehensive tests" \
  "Generate API documentation" \
  --timeout 60 \
  --verbose

Code Review Pipeline

# Automated code review workflow
claude-flow stream-chain run \
  "Analyze recent git changes" \
  "Identify code quality issues" \
  "Check for security vulnerabilities" \
  "Verify test coverage" \
  "Generate code review report with recommendations"

Migration Assistant

# Framework migration helper
claude-flow stream-chain run \
  "Analyze current Vue 2 codebase" \
  "Identify Vue 3 breaking changes" \
  "Create migration checklist" \
  "Generate migration scripts" \
  "Provide updated code examples"

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Conclusion

Stream-Chain enables sophisticated multi-step workflows by:

• Sequential Processing: Each step builds on previous results
• Context Preservation: Full output history flows through chain
• Flexible Orchestration: Custom chains or predefined pipelines
• Agent Coordination: Natural multi-agent collaboration pattern
• Data Transformation: Complex processing through simple steps

Use run for custom workflows and pipeline for battle-tested solutions.