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Envío de agentes paralelos

Envío de agentes paralelos

Distribuye tareas complejas entre múltiples agentes de IA que trabajan simultáneamente.

PROMPT EXAMPLE
Usa `dispatching-parallel-agents` para aumentar la velocidad.
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SKILL.md Definition

Dispatching Parallel Agents

Overview

When you have multiple unrelated failures (different test files, different subsystems, different bugs), investigating them sequentially wastes time. Each investigation is independent and can happen in parallel.

Core principle: Dispatch one agent per independent problem domain. Let them work concurrently.

When to Use

digraph when_to_use {
    "Multiple failures?" [shape=diamond];
    "Are they independent?" [shape=diamond];
    "Single agent investigates all" [shape=box];
    "One agent per problem domain" [shape=box];
    "Can they work in parallel?" [shape=diamond];
    "Sequential agents" [shape=box];
    "Parallel dispatch" [shape=box];

    "Multiple failures?" -> "Are they independent?" [label="yes"];
    "Are they independent?" -> "Single agent investigates all" [label="no - related"];
    "Are they independent?" -> "Can they work in parallel?" [label="yes"];
    "Can they work in parallel?" -> "Parallel dispatch" [label="yes"];
    "Can they work in parallel?" -> "Sequential agents" [label="no - shared state"];
}

Use when:

  • 3+ test files failing with different root causes
  • Multiple subsystems broken independently
  • Each problem can be understood without context from others
  • No shared state between investigations

Don't use when:

  • Failures are related (fix one might fix others)
  • Need to understand full system state
  • Agents would interfere with each other

The Pattern

1. Identify Independent Domains

Group failures by what's broken:

  • File A tests: Tool approval flow
  • File B tests: Batch completion behavior
  • File C tests: Abort functionality

Each domain is independent - fixing tool approval doesn't affect abort tests.

2. Create Focused Agent Tasks

Each agent gets:

  • Specific scope: One test file or subsystem
  • Clear goal: Make these tests pass
  • Constraints: Don't change other code
  • Expected output: Summary of what you found and fixed

3. Dispatch in Parallel

// In Claude Code / AI environment
Task("Fix agent-tool-abort.test.ts failures")
Task("Fix batch-completion-behavior.test.ts failures")
Task("Fix tool-approval-race-conditions.test.ts failures")
// All three run concurrently

4. Review and Integrate

When agents return:

  • Read each summary
  • Verify fixes don't conflict
  • Run full test suite
  • Integrate all changes

Agent Prompt Structure

Good agent prompts are:

  1. Focused - One clear problem domain
  2. Self-contained - All context needed to understand the problem
  3. Specific about output - What should the agent return?
Fix the 3 failing tests in src/agents/agent-tool-abort.test.ts:

1. "should abort tool with partial output capture" - expects 'interrupted at' in message
2. "should handle mixed completed and aborted tools" - fast tool aborted instead of completed
3. "should properly track pendingToolCount" - expects 3 results but gets 0

These are timing/race condition issues. Your task:

1. Read the test file and understand what each test verifies
2. Identify root cause - timing issues or actual bugs?
3. Fix by:
   - Replacing arbitrary timeouts with event-based waiting
   - Fixing bugs in abort implementation if found
   - Adjusting test expectations if testing changed behavior

Do NOT just increase timeouts - find the real issue.

Return: Summary of what you found and what you fixed.

Common Mistakes

❌ Too broad: "Fix all the tests" - agent gets lost ✅ Specific: "Fix agent-tool-abort.test.ts" - focused scope

❌ No context: "Fix the race condition" - agent doesn't know where ✅ Context: Paste the error messages and test names

❌ No constraints: Agent might refactor everything ✅ Constraints: "Do NOT change production code" or "Fix tests only"

❌ Vague output: "Fix it" - you don't know what changed ✅ Specific: "Return summary of root cause and changes"

When NOT to Use

Related failures: Fixing one might fix others - investigate together first Need full context: Understanding requires seeing entire system Exploratory debugging: You don't know what's broken yet Shared state: Agents would interfere (editing same files, using same resources)

Real Example from Session

Scenario: 6 test failures across 3 files after major refactoring

Failures:

  • agent-tool-abort.test.ts: 3 failures (timing issues)
  • batch-completion-behavior.test.ts: 2 failures (tools not executing)
  • tool-approval-race-conditions.test.ts: 1 failure (execution count = 0)

Decision: Independent domains - abort logic separate from batch completion separate from race conditions

Dispatch:

Agent 1 → Fix agent-tool-abort.test.ts
Agent 2 → Fix batch-completion-behavior.test.ts
Agent 3 → Fix tool-approval-race-conditions.test.ts

Results:

  • Agent 1: Replaced timeouts with event-based waiting
  • Agent 2: Fixed event structure bug (threadId in wrong place)
  • Agent 3: Added wait for async tool execution to complete

Integration: All fixes independent, no conflicts, full suite green

Time saved: 3 problems solved in parallel vs sequentially

Key Benefits

  1. Parallelization - Multiple investigations happen simultaneously
  2. Focus - Each agent has narrow scope, less context to track
  3. Independence - Agents don't interfere with each other
  4. Speed - 3 problems solved in time of 1

Verification

After agents return:

  1. Review each summary - Understand what changed
  2. Check for conflicts - Did agents edit same code?
  3. Run full suite - Verify all fixes work together
  4. Spot check - Agents can make systematic errors

Real-World Impact

From debugging session (2025-10-03):

  • 6 failures across 3 files
  • 3 agents dispatched in parallel
  • All investigations completed concurrently
  • All fixes integrated successfully
  • Zero conflicts between agent changes

About Superpowers

Superpowers is a complete software development workflow for your coding agents, built on top of a set of composable "skills".

Philosophy

  • Test-Driven Development - Write tests first, always
  • Systematic over ad-hoc - Process over guessing
  • Complexity reduction - Simplicity as primary goal
  • Evidence over claims - Verify before declaring success

Installation

Note: Installation differs by platform. Claude Code has a built-in plugin system. Codex and OpenCode require manual setup.

Claude Code (via Plugin Marketplace)

In Claude Code, register the marketplace first:

/plugin marketplace add obra/superpowers-marketplace

Then install the plugin from this marketplace:

/plugin install superpowers@superpowers-marketplace

Verify Installation

Check that commands appear:

/help
# Should see:
# /superpowers:brainstorm - Interactive design refinement
# /superpowers:write-plan - Create implementation plan
# /superpowers:execute-plan - Execute plan in batches

Potentes Agent Skills

Impulsa el rendimiento de tu IA con nuestra colección de habilidades profesionales.

Listo para usar

Copia y pega en cualquier sistema de agente que admita habilidades.

Diseño modular

Combina 'code skills' para crear comportamientos de agente complejos.

Optimizado

Cada 'agent skill' está ajustado para un alto rendimiento y precisión.

Código abierto

Todos los 'code skills' están abiertos a contribuciones y personalización.

Multiplataforma

Funciona con varios LLM y marcos de agentes.

Seguro y fiable

Habilidades verificadas que siguen las mejores prácticas de seguridad de IA.

Potencia a tus agentes

Comienza a usar Agiskills hoy mismo y nota la diferencia.

Explorar ahora

Cómo funciona

Comienza con las habilidades de agente en tres sencillos pasos.

1

Elige una habilidad

Encuentra la habilidad que necesitas en nuestra colección.

2

Lee la documentación

Comprende cómo funciona la habilidad y sus limitaciones.

3

Copia y utiliza

Pega la definición en la configuración de tu agente.

4

Prueba

Verifica los resultados y ajusta si es necesario.

5

Despliega

Lanza tu agente de IA especializado.

Lo que dicen los desarrolladores

Descubre por qué desarrolladores de todo el mundo eligen Agiskills.

Alex Smith

Ingeniero de IA

"Agiskills ha cambiado por completo la forma en que construyo agentes de IA."

Maria Garcia

Gerente de producto

"La habilidad PDF Specialist resolvió problemas complejos de análisis de documentos para nosotros."

John Doe

Desarrollador

"Habilidades profesionales y bien documentadas. ¡Muy recomendable!"

Sarah Lee

Artista

"La habilidad de Arte Algorítmico produce un código increíblemente hermoso."

Chen Wei

Especialista en Frontend

"Los temas generados por Theme Factory son perfectos hasta el último píxel."

Robert T.

CTO

"Ahora usamos Agiskills como el estándar para nuestro equipo de IA."

Preguntas frecuentes

Todo lo que necesitas saber sobre Agiskills.

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