Agent skill

parallel

Run multiple Ralph loops concurrently for independent tasks. Supports all 6 ralph-* teammates (coder, reviewer, tester, researcher, frontend, security). Manages parallel agent execution with proper isolation and result aggregation. Use when: (1) multiple independent fixes needed, (2) parallel reviews required, (3) batch processing tasks. Triggers: /parallel, 'parallel loops', 'concurrent execution', 'run in parallel', 'batch'.

Stars 115
Forks 18

Install this agent skill to your Project

npx add-skill https://github.com/alfredolopez80/multi-agent-ralph-loop/tree/main/.claude/skills/parallel

SKILL.md

Parallel - Concurrent Execution (v3.0)

Run multiple Ralph loops concurrently for independent tasks.

v2.88 Key Changes (MODEL-AGNOSTIC)

  • Model-agnostic: Uses model configured in ~/.claude/settings.json or CLI/env vars
  • No flags required: All parallel tasks use the configured default model
  • Flexible: Works with GLM-5, Claude, Minimax, or any configured model
  • Settings-driven: Model selection via ANTHROPIC_DEFAULT_*_MODEL env vars

Agent Teams Integration (v2.88)

Optimal Scenario: Integrated (Agent Teams + Custom Subagents)

Parallel execution combines Agent Teams coordination with ralph-coder specialization for optimal parallel file processing.

Why Scenario C for Parallel Execution

  • Multiple files require coordinated distribution
  • Quality gates essential for result validation
  • Specialized ralph-coder agents for implementation
  • Shared task list tracks all parallel work

Automatic Team Creation

When the parallel skill is invoked, automatically create a team:

yaml
# Automatically create team on skill invocation
TeamCreate:
  team_name: "parallel-execution-{timestamp}"
  description: "Parallel execution of independent tasks"

Spawning Parallel Agents

Create multiple ralph-coder instances for parallel tasks:

yaml
# Spawn 3 parallel ralph-coder agents
Task:
  subagent_type: "ralph-coder"
  team_name: "parallel-execution-{timestamp}"
  prompt: "Fix auth errors in src/auth/"

Task:
  subagent_type: "ralph-coder"
  team_name: "parallel-execution-{timestamp}"
  prompt: "Fix API errors in src/api/"

Task:
  subagent_type: "ralph-coder"
  team_name: "parallel-execution-{timestamp}"
  prompt: "Fix UI errors in src/ui/"

Task Coordination Pattern

Use the shared task list for coordination:

yaml
# Create master task list
TaskCreate:
  subject: "Parallel fixes batch"
  description: "Execute auth, API, and UI fixes in parallel"

# Create subtasks with dependencies
TaskCreate:
  subject: "Fix auth errors"
  activeForm: "Fixing auth errors"

TaskCreate:
  subject: "Fix API errors"
  activeForm: "Fixing API errors"

TaskCreate:
  subject: "Fix UI errors"
  activeForm: "Fixing UI errors"

Quality Gates

Quality validation via Agent Teams hooks:

Hook Purpose Behavior
TeammateIdle Pre-idle validation Keep working + feedback if issues found
TaskCompleted Pre-completion validation Block completion + feedback if issues found

Quality standards enforced:

  1. CORRECTNESS: Valid syntax, sound logic
  2. QUALITY: No console.log, proper types, no TODOs
  3. SECURITY: No hardcoded secrets, proper validation
  4. CONSISTENCY: Follow project style guides

Result Aggregation

After all parallel agents complete:

yaml
# Aggregate results
TaskUpdate:
  taskId: "<master-task>"
  status: "completed"

# Report summary
- All subtasks completed
- Quality gates passed
- Changes ready for commit

Quick Start

bash
/parallel "fix auth errors" "fix api errors" "fix ui errors"
ralph parallel task1 task2 task3

When to Use

Good for Parallel

  • Independent file changes
  • Multiple module fixes
  • Batch reviews
  • Different analysis types

Must Be Sequential

  • Dependent changes
  • Same file modifications
  • Order-dependent operations
  • Shared state changes

Workflow

1. Create Team and Spawn Agents

yaml
# Auto-create team for coordination
TeamCreate:
  team_name: "parallel-{task-name}"
  description: "Parallel execution of {task-name}"

# Launch multiple ralph-coder background agents
Task:
  subagent_type: "ralph-coder"
  team_name: "parallel-{task-name}"
  prompt: "Execute task 1"

Task:
  subagent_type: "ralph-coder"
  team_name: "parallel-{task-name}"
  prompt: "Execute task 2"

Task:
  subagent_type: "ralph-coder"
  team_name: "parallel-{task-name}"
  prompt: "Execute task 3"

2. Monitor Progress

yaml
# Check task list for all subtask status
TaskList:
  # Returns all tasks with status, owner, blockedBy

# Monitor specific subtask
TaskGet:
  taskId: "<subtask-id>"

3. Aggregate Results

yaml
# Mark master task as completed
TaskUpdate:
  taskId: "<master-task>"
  status: "completed"

# Report summary
- All subtasks completed
- Quality gates passed
- Changes ready for commit

Parallel Patterns

Review Pattern

bash
# Parallel reviews with different focus
/parallel "security review src/" "performance review src/" "quality review src/"

Fix Pattern

bash
# Parallel fixes for different modules
/parallel "fix auth errors" "fix api errors" "fix db errors"

Analysis Pattern

bash
# Parallel analysis tasks
/parallel "analyze complexity" "analyze coverage" "analyze dependencies"

Isolation

Each parallel task runs with:

  • Separate context (context: fork)
  • Independent iteration counter
  • Own quality gates
  • Isolated file access

Result Aggregation

All Succeed

  • Aggregate changes
  • Run global gates
  • VERIFIED_DONE

Partial Success

  • Report failures
  • Keep successful changes
  • Retry failed tasks

All Fail

  • Report all errors
  • Analyze patterns
  • Sequential retry

Integration

  • Used for independent sub-tasks
  • Each parallel task follows Ralph Loop
  • Results feed back to orchestrator

Anti-Patterns

  • Never run parallel on same files
  • Never exceed 5 concurrent agents
  • Never ignore partial failures
  • Never skip aggregation step

Action Reporting (v2.93.0)

Esta skill genera reportes automáticos completos para trazabilidad:

Reporte Automático

Cuando esta skill completa, se genera automáticamente:

  1. En la conversación de Claude: Resultados visibles
  2. En el repositorio: docs/actions/parallel/{timestamp}.md
  3. Metadatos JSON: .claude/metadata/actions/parallel/{timestamp}.json

Contenido del Reporte

Cada reporte incluye:

  • Summary: Descripción de la tarea ejecutada
  • Execution Details: Duración, iteraciones, archivos modificados
  • Results: Errores encontrados, recomendaciones
  • Next Steps: Próximas acciones sugeridas

Ver Reportes Anteriores

bash
# Listar todos los reportes de esta skill
ls -lt docs/actions/parallel/

# Ver el reporte más reciente
cat $(ls -t docs/actions/parallel/*.md | head -1)

# Buscar reportes fallidos
grep -l "Status: FAILED" docs/actions/parallel/*.md

Generación Manual (Opcional)

bash
source .claude/lib/action-report-lib.sh
start_action_report "parallel" "Task description"
# ... ejecución ...
complete_action_report "success" "Summary" "Recommendations"

Referencias del Sistema

  • Action Reports System - Documentación completa
  • action-report-lib.sh - Librería helper
  • action-report-generator.sh - Generador

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