ComeOnOliver 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 workflowsmulti-impl.md: Code generation with selectiondebug-consensus.md: Multi-diagnosis bug fixing
Reference
scoring-rubrics.md: Detailed scoring criteriafile-conventions.md: Output file standards
Related Skills
spawn: Provider-specific CLI invocation patternsmulti-agent-orchestration: General multi-agent patternsresearch: Parallel research with synthesis