stream-chain

name	stream-chain
description	Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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

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

Execute a Pipeline

Codex-flow stream-chain pipeline analysis

---

Custom Chains (run)

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

Syntax

Codex-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

Option	Description	Default
--verbose	Show detailed execution information	false
--timeout <seconds>	Timeout per step	30
--debug	Enable debug mode with full logging	false

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

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

Security Audit Workflow

Codex-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

Codex-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

Codex-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

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

Available Pipelines

1. Analysis Pipeline

Comprehensive codebase analysis and improvement identification.

Codex-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.

Codex-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.

Codex-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.

Codex-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

Option	Description	Default
--verbose	Show detailed execution	false
--timeout <seconds>	Timeout per step	30
--debug	Enable debug mode	false

Pipeline Examples

Quick Analysis

Codex-flow stream-chain pipeline analysis

Extended Refactoring

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

Debug Test Generation

Codex-flow stream-chain pipeline test --debug

Comprehensive Optimization

Codex-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

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Custom Pipeline Definitions

Define reusable pipelines in .Codex-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

Codex-flow stream-chain pipeline security
Codex-flow stream-chain pipeline documentation

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Advanced Use Cases

Multi-Agent Coordination

Chain different agent types for complex workflows:

Codex-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:

Codex-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:

Codex-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:

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

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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:

Codex-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:

Codex-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 Codex Flow

Combine with Swarm Coordination

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

# Execute stream chain with swarm agents
Codex-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
Codex-flow stream-chain run \
  "Analyze requirements" \
  "Design architecture" \
  --verbose

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

Neural Pattern Training

Successful chains train neural patterns for improved performance:

# Enable neural training
Codex-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:

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

Context Loss

If context not flowing properly, use --debug:

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

Pipeline Not Found

Verify pipeline name and custom definitions:

# Check available pipelines
cat .Codex-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
Codex-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
Codex-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
Codex-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.