Skills Agentes

Stream Chain

Encadenamiento de streams JSON para pipelines multi-agente, transformación de datos y flujos de trabajo secuenciales.

Estrellas
69.4k

en todo el repo

Actividad
27

0–100, la ruta de este skill

Actualizado
hace 6 meses

último commit aquí

Commits
0

últimos 90 días

Contexto
3.2k tok

23 tok en reposo

Paquete
1 archivo

12 KB

Instalar

Funciona con cualquier agente que lea SKILL.md

npx -y skills add ruvnet/ruflo --skill stream-chain --agent claude-code

Se instala solo en este repositorio.

Qué hace

  • Ejecuta cadenas de prompts personalizadas donde la salida de cada paso alimenta al siguiente.
  • Ofrece pipelines predefinidos y probados: analysis, refactor, test y optimize.
  • Permite definir pipelines personalizados reutilizables en la configuración del proyecto.

Úsalo cuando

  • Necesitas un flujo de trabajo multi-paso donde cada etapa depende de la anterior.
  • Quieres analizar, refactorizar, testear u optimizar código con un pipeline predefinido.
  • Vas a coordinar múltiples agentes en una secuencia de transformación de datos.

No lo uses cuando

    Qué lo activa

    Di cualquiera de estas frases y el agente debería cargar este skill.

    • Ejecuta el pipeline de análisis sobre este código
    • Corre una cadena: identifica code smells, crea plan de refactor y aplícalo
    • Ejecuta el pipeline de optimize con timeout de 90 segundos

    SKILL.md

    En inglés

    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

    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

    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

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

    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.

    Reproducido de ruvnet/ruflo bajo licencia MIT. Leer esta página en markdown.

    Archivos

    1 archivo en el paquete. Solo se lee SKILL.md al activarse — las referencias se cargan si el skill decide que las necesita.

    Detalles

    Creador
    ruvnet
    Categoría
    Automatización
    Licencia
    MIT
    Recursos incluidos
    Solo SKILL.md
    Repositorio
    ruvnet/ruflo
    Código fuente
    Ver SKILL.md

    Etiquetas

    Más de ruvnet/ruflo

    Este repo incluye 275 skills. Si instalas uno, normalmente ya tienes los demás.

    Inspecciona y audita genomas GEPA: carga y valida un genoma, renderiza el system prompt que compila, o clasifica los modos de fallo de una transcripción de ejecución.

    Costo de contexto al activarse
    833 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 4 días
    Permisos
    herramientas desarrollo

    Completion de un solo turno contra el modelo deepseek-chat de DeepSeek vía /v1/chat/completions. Lee DEEPSEEK_API_KEY y degrada con status:degraded si falta o la API no responde. Para tareas sin razonamiento.

    Costo de contexto al activarse
    566 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 4 días
    Permisos
    automatizacion

    Completion en modo razonamiento contra deepseek-reasoner (R1) de DeepSeek. Devuelve el chain-of-thought por separado de la respuesta final. Lee DEEPSEEK_API_KEY y degrada si falta o la API no responde.

    Costo de contexto al activarse
    627 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 4 días
    Permisos
    automatizacion

    Construye o reconstruye el índice de ADRs y su grafo de dependencias ejecutando scripts/import.mjs, en vez de cientos de llamadas MCP.

    Costo de contexto al activarse
    866 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 27 días
    herramientas desarrollo

    Crea un nuevo Architecture Decision Record con numeración secuencial y registro en AgentDB.

    Costo de contexto al activarse
    680 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 27 días
    herramientas desarrollo

    Muestra el estado de la integración AGNTCY/SLIM/CASA: si los paquetes están instalados, qué transporte está activo y si el enforcement de CASA está habilitado.

    Costo de contexto al activarse
    443 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 26 días
    devops infraestructura

    Skills relacionados

    Coordinador de swarm que cambia dinámicamente de topología (jerárquica, malla, anillo, híbrida) y se reoptimiza en tiempo real según patrones de rendimiento y carga de trabajo, con aprendizaje integrado.

    Costo de contexto al activarse
    4k tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 6 meses
    automatizacion

    Especialista en Goal-Oriented Action Planning (GOAP) que crea planes dinámicos e inteligentes para objetivos complejos, combinando técnicas de IA de videojuegos, replanificación adaptativa y optimización sublineal.

    Costo de contexto al activarse
    6.3k tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 6 meses
    automatizacion

    Especialista en coordinación inteligente y generación dinámica de agentes: analiza tareas y crea automáticamente los agentes con las capacidades óptimas para cada trabajo.

    Costo de contexto al activarse
    1.4k tok
    Tamaño del paquete
    1 archivo
    Última actualización
    hace 6 meses
    automatizacion