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stream-chain

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

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npx tessl i github:ruvnet/ruvector --skill stream-chain
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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.

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

Custom Chains (run)

Execute custom stream chains with your own prompts for maximum flexibility.

Syntax

claude-flow stream-chain run <prompt1> <prompt2> [...] [options]

Requirements:

  • Minimum 2 prompts required
  • Each prompt becomes a step in the chain
  • Output flows sequentially through all steps

Options

OptionDescriptionDefault
--verboseShow detailed execution informationfalse
--timeout <seconds>Timeout per step30
--debugEnable debug mode with full loggingfalse

How Context Flows

Each step receives the previous output as context:

Step 1: "Write a sorting function"
Output: [function implementation]

Step 2 receives:
  "Previous step output:
  [function implementation]

  Next task: Add comprehensive tests"

Step 3 receives:
  "Previous steps output:
  [function + tests]

  Next task: Optimize performance"

Examples

Basic Development Chain

claude-flow stream-chain run \
  "Write a user authentication function" \
  "Add input validation and error handling" \
  "Create unit tests with edge cases"

Security Audit Workflow

claude-flow stream-chain run \
  "Analyze authentication system for vulnerabilities" \
  "Identify and categorize security issues by severity" \
  "Propose fixes with implementation priority" \
  "Generate security test cases" \
  --timeout 45 \
  --verbose

Code Refactoring Chain

claude-flow stream-chain run \
  "Identify code smells in src/ directory" \
  "Create refactoring plan with specific changes" \
  "Apply refactoring to top 3 priority items" \
  "Verify refactored code maintains behavior" \
  --debug

Data Processing Pipeline

claude-flow stream-chain run \
  "Extract data from API responses" \
  "Transform data into normalized format" \
  "Validate data against schema" \
  "Generate data quality report"

Predefined Pipelines (pipeline)

Execute battle-tested workflows optimized for common development tasks.

Syntax

claude-flow stream-chain pipeline <type> [options]

Available Pipelines

1. Analysis Pipeline

Comprehensive codebase analysis and improvement identification.

claude-flow stream-chain pipeline analysis

Workflow Steps:

  1. Structure Analysis: Map directory structure and identify components
  2. Issue Detection: Find potential improvements and problems
  3. Recommendations: Generate actionable improvement report

Use Cases:

  • New codebase onboarding
  • Technical debt assessment
  • Architecture review
  • Code quality audits

2. Refactor Pipeline

Systematic code refactoring with prioritization.

claude-flow stream-chain pipeline refactor

Workflow Steps:

  1. Candidate Identification: Find code needing refactoring
  2. Prioritization: Create ranked refactoring plan
  3. Implementation: Provide refactored code for top priorities

Use Cases:

  • Technical debt reduction
  • Code quality improvement
  • Legacy code modernization
  • Design pattern implementation

3. Test Pipeline

Comprehensive test generation with coverage analysis.

claude-flow stream-chain pipeline test

Workflow Steps:

  1. Coverage Analysis: Identify areas lacking tests
  2. Test Design: Create test cases for critical functions
  3. Implementation: Generate unit tests with assertions

Use Cases:

  • Increasing test coverage
  • TDD workflow support
  • Regression test creation
  • Quality assurance

4. Optimize Pipeline

Performance optimization with profiling and implementation.

claude-flow stream-chain pipeline optimize

Workflow Steps:

  1. Profiling: Identify performance bottlenecks
  2. Strategy: Analyze and suggest optimization approaches
  3. Implementation: Provide optimized code

Use Cases:

  • Performance improvement
  • Resource optimization
  • Scalability enhancement
  • Latency reduction

Pipeline Options

OptionDescriptionDefault
--verboseShow detailed executionfalse
--timeout <seconds>Timeout per step30
--debugEnable debug modefalse

Pipeline Examples

Quick Analysis

claude-flow stream-chain pipeline analysis

Extended Refactoring

claude-flow stream-chain pipeline refactor --timeout 60 --verbose

Debug Test Generation

claude-flow stream-chain pipeline test --debug

Comprehensive Optimization

claude-flow stream-chain pipeline optimize --timeout 90 --verbose

Pipeline Output

Each pipeline execution provides:

  • Progress: Step-by-step execution status
  • Results: Success/failure per step
  • Timing: Total and per-step execution time
  • Summary: Consolidated results and recommendations

Custom Pipeline Definitions

Define reusable pipelines in .claude-flow/config.json:

Configuration Format

{
  "streamChain": {
    "pipelines": {
      "security": {
        "name": "Security Audit Pipeline",
        "description": "Comprehensive security analysis",
        "prompts": [
          "Scan codebase for security vulnerabilities",
          "Categorize issues by severity (critical/high/medium/low)",
          "Generate fixes with priority and implementation steps",
          "Create security test suite"
        ],
        "timeout": 45
      },
      "documentation": {
        "name": "Documentation Generation Pipeline",
        "prompts": [
          "Analyze code structure and identify undocumented areas",
          "Generate API documentation with examples",
          "Create usage guides and tutorials",
          "Build architecture diagrams and flow charts"
        ]
      }
    }
  }
}

Execute Custom Pipeline

claude-flow stream-chain pipeline security
claude-flow stream-chain pipeline documentation

Advanced Use Cases

Multi-Agent Coordination

Chain different agent types for complex workflows:

claude-flow stream-chain run \
  "Research best practices for API design" \
  "Design REST API with discovered patterns" \
  "Implement API endpoints with validation" \
  "Generate OpenAPI specification" \
  "Create integration tests" \
  "Write deployment documentation"

Data Transformation Pipeline

Process and transform data through multiple stages:

claude-flow stream-chain run \
  "Extract user data from CSV files" \
  "Normalize and validate data format" \
  "Enrich data with external API calls" \
  "Generate analytics report" \
  "Create visualization code"

Code Migration Workflow

Systematic code migration with validation:

claude-flow stream-chain run \
  "Analyze legacy codebase dependencies" \
  "Create migration plan with risk assessment" \
  "Generate modernized code for high-priority modules" \
  "Create migration tests" \
  "Document migration steps and rollback procedures"

Quality Assurance Chain

Comprehensive code quality workflow:

claude-flow stream-chain pipeline analysis
claude-flow stream-chain pipeline refactor
claude-flow stream-chain pipeline test
claude-flow stream-chain pipeline optimize

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"

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

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"

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

Related Skills

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

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"

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.

Repository
ruvnet/ruvector
Commit

241ccd1

Last updated
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