Parallel - Concurrent Execution (v3.0)

SkillDev tools

Parallel execution patterns for montaj — load when workflow has multiple clips or foreach steps

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Parallel - Concurrent Execution (v3.0) skill

What this skill tells your AI

The instructions your AI receives, as published by thesampadilla/montaj in skills/parallel/SKILL.md and read by ahel’s review.

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: Model-agnostic — runs on whatever model the session runs
  • 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:

# 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:

# 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:

# 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:

HookPurposeBehavior
TeammateIdlePre-idle validationKeep working + feedback if issues found
TaskCompletedPre-completion validationBlock 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:

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

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

Quick Start

/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

# 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

# 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

# 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

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

Fix Pattern

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

Analysis Pattern

# 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

Constraints

  • Parallel loops touch disjoint file sets; two agents editing one file produce silent overwrites.
  • At most 5 concurrent agents, past which rate limits and context contention dominate.
  • A partial failure fails the batch; aggregate every loop's result before reporting.

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

# 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)

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

Referencias del Sistema

Signals

GitHub stars
25
Forks
11
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
parallel
Source
github.com/thesampadilla/montaj