mapreduce

name	mapreduce
description	Fan-out tasks to multiple AI providers or agent instances in parallel, then consolidate the results into a single high-quality output. Use for parallel planning, multi-implementation code generation, and debug consensus across Claude subagents and external CLI providers.

MapReduce Skill

Skill ID: mapreduce Purpose: Fan-out tasks to multiple providers/agents, then consolidate results Category: Orchestration

Overview

The MapReduce skill enables parallel task execution across multiple AI providers or agent instances, followed by intelligent consolidation of results. This produces higher-quality outputs by leveraging diverse model strengths and cross-validating findings.

Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                      MAIN THREAD (Orchestrator)                          │
│                                                                         │
│  ┌─────────────────────────────────────────────────────────────────┐    │
│  │  PHASE 1: MAP (Parallel Fan-Out)                                │    │
│  │                                                                 │    │
│  │  Task(worker-1) ──→ output-1.md                                │    │
│  │  Task(worker-2) ──→ output-2.md                                │    │
│  │  Task(worker-3) ──→ output-3.md                                │    │
│  │  bash(codex)   ──→ output-codex.md                             │    │
│  │  bash(gemini)  ──→ output-gemini.md                            │    │
│  └─────────────────────────────────────────────────────────────────┘    │
│                              │                                          │
│                              ▼                                          │
│  ┌─────────────────────────────────────────────────────────────────┐    │
│  │  PHASE 2: COLLECT (Timeout-Based)                               │    │
│  │                                                                 │    │
│  │  TaskOutput(worker-1, timeout=120s)                            │    │
│  │  TaskOutput(worker-2, timeout=120s)                            │    │
│  │  TaskOutput(worker-3, timeout=120s)                            │    │
│  │  Verify: output-codex.md, output-gemini.md exist               │    │
│  └─────────────────────────────────────────────────────────────────┘    │
│                              │                                          │
│                              ▼                                          │
│  ┌─────────────────────────────────────────────────────────────────┐    │
│  │  PHASE 3: REDUCE (Consolidation)                                │    │
│  │                                                                 │    │
│  │  Task(reducer) ──→ reads all outputs ──→ consolidated.md       │    │
│  └─────────────────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────────────────┘

Key Constraint

Subagents cannot spawn other subagents. All orchestration happens in the main thread. Workers and reducers are subagents that operate on files.

Use Cases

1. Parallel Planning

Fan out planning task to multiple providers with different strategic biases:

Workers:
  - planner-conservative: Low-risk, proven patterns
  - planner-aggressive: Fast-track, modern patterns
  - planner-security: Security-first approach

Reducer: plan-reducer
Output: specs/ROADMAP.md

See: cookbook/parallel-planning.md

2. Multi-Implementation

Generate the same feature with multiple models, pick best:

Workers:
  - impl-claude: Claude's implementation
  - impl-codex: OpenAI's implementation
  - impl-gemini: Gemini's implementation

Reducer: code-reducer
Output: src/feature/implementation.ts

See: cookbook/multi-impl.md

3. Debug Consensus

Get multiple diagnoses of a bug, verify and select best fix:

Workers:
  - debug-claude: Claude's diagnosis
  - debug-codex: Codex's diagnosis
  - debug-gemini: Gemini's diagnosis

Reducer: debug-reducer
Output: Applied fix + documentation

See: cookbook/debug-consensus.md

Available Reducers

Reducer	Agent Path	Purpose
plan-reducer	agents/orchestration/reducers/plan-reducer.md	Consolidate plans
code-reducer	agents/orchestration/reducers/code-reducer.md	Compare/merge code
debug-reducer	agents/orchestration/reducers/debug-reducer.md	Verify fixes

Provider Integration

Claude Subagents (via Task tool)

Task(subagent_type="Plan", prompt="...", run_in_background=true)

External CLI Providers (via spawn skill)

# Codex
codex -m gpt-5.1-codex -a full-auto "${PROMPT}" > output.md

# Gemini
gemini -m gemini-3-pro "${PROMPT}" > output.md

# Cursor
cursor-agent --mode print "${PROMPT}" > output.md

# OpenCode
opencode --provider anthropic "${PROMPT}" > output.md

See: skills/spawn/agent/cookbook/ for detailed CLI patterns.

File Conventions

All MapReduce operations follow standard file conventions:

Type	Location	Naming
Plan outputs	specs/plans/	planner-{name}.md
Code outputs	implementations/	impl-{name}.{ext}
Debug outputs	diagnoses/	debug-{name}.md
Consolidated	Specified in prompt	ROADMAP.md, implementation.ts

See: reference/file-conventions.md

Scoring Rubrics

Each reducer uses a specific scoring rubric:

• Plans: Completeness, Feasibility, Risk, Clarity, Innovation
• Code: Correctness, Readability, Maintainability, Performance, Security
• Debug: Correctness, Minimality, Safety, Clarity, Root Cause

See: reference/scoring-rubrics.md

Commands

Command	Purpose
/ai-dev-kit:mapreduce	Full MapReduce workflow
/ai-dev-kit:map	Just the fan-out phase
/ai-dev-kit:reduce	Just the consolidation phase

Example: Full MapReduce

# In main thread:

## Step 1: MAP

Launch planners in a single message (enables parallelism):

Task(subagent_type="Plan", prompt="""
  Create implementation plan for: User Authentication
  Write to: specs/plans/planner-conservative.md
  Strategy: Conservative - proven patterns, minimal risk
""", run_in_background=true)

Task(subagent_type="Plan", prompt="""
  Create implementation plan for: User Authentication
  Write to: specs/plans/planner-aggressive.md
  Strategy: Aggressive - fast, modern patterns
""", run_in_background=true)

Bash("codex -m gpt-5.1-codex -a full-auto 'Create auth plan' > specs/plans/planner-codex.md")

## Step 2: COLLECT

TaskOutput(task_id=conservative-id, block=true, timeout=120000)
TaskOutput(task_id=aggressive-id, block=true, timeout=120000)

# Verify codex output exists
Read("specs/plans/planner-codex.md")

## Step 3: REDUCE

Task(subagent_type="ai-dev-kit:orchestration:plan-reducer", prompt="""
  Consolidate plans in specs/plans/*.md
  Output: specs/ROADMAP.md
  Priority: Security over speed
""")

Cookbook

• parallel-planning.md: Multi-provider planning workflows
• multi-impl.md: Code generation with selection
• debug-consensus.md: Multi-diagnosis bug fixing

Reference

• scoring-rubrics.md: Detailed scoring criteria
• file-conventions.md: Output file standards

Related Skills

• spawn: Provider-specific CLI invocation patterns
• multi-agent-orchestration: General multi-agent patterns
• research: Parallel research with synthesis