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This produces higher-quality outputs by levera...","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fconsiliency\u002Fmapreduce\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33],"Providers","execution","parallel","quality","results","instances","task","parallel task","across","consolidation","2026-11-10 10:12:30",false,"---\nname: mapreduce\ndescription: 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 levera...\n---\n\n# MapReduce Skill\n\n> **Skill ID**: mapreduce\n> **Purpose**: Fan-out tasks to multiple providers\u002Fagents, then consolidate results\n> **Category**: Orchestration\n\n## Overview\n\nThe MapReduce skill enables parallel task execution across multiple AI providers\nor agent instances, followed by intelligent consolidation of results. This\nproduces higher-quality outputs by leveraging diverse model strengths and\ncross-validating findings.\n\n## Architecture\n\n```\n┌─────────────────────────────────────────────────────────────────────────┐\n│                      MAIN THREAD (Orchestrator)                          │\n│                                                                         │\n│  ┌─────────────────────────────────────────────────────────────────┐    │\n│  │  PHASE 1: MAP (Parallel Fan-Out)                                │    │\n│  │                                                                 │    │\n│  │  Task(worker-1) ──→ output-1.md                                │    │\n│  │  Task(worker-2) ──→ output-2.md                                │    │\n│  │  Task(worker-3) ──→ output-3.md                                │    │\n│  │  bash(codex)   ──→ output-codex.md                             │    │\n│  │  bash(gemini)  ──→ output-gemini.md                            │    │\n│  └─────────────────────────────────────────────────────────────────┘    │\n│                              │                                          │\n│                              ▼                                          │\n│  ┌─────────────────────────────────────────────────────────────────┐    │\n│  │  PHASE 2: COLLECT (Timeout-Based)                               │    │\n│  │                                                                 │    │\n│  │  TaskOutput(worker-1, timeout=120s)                            │    │\n│  │  TaskOutput(worker-2, timeout=120s)                            │    │\n│  │  TaskOutput(worker-3, timeout=120s)                            │    │\n│  │  Verify: output-codex.md, output-gemini.md exist               │    │\n│  └─────────────────────────────────────────────────────────────────┘    │\n│                              │                                          │\n│                              ▼                                          │\n│  ┌─────────────────────────────────────────────────────────────────┐    │\n│  │  PHASE 3: REDUCE (Consolidation)                                │    │\n│  │                                                                 │    │\n│  │  Task(reducer) ──→ reads all outputs ──→ consolidated.md       │    │\n│  └─────────────────────────────────────────────────────────────────┘    │\n└─────────────────────────────────────────────────────────────────────────┘\n```\n\n## Key Constraint\n\n**Subagents cannot spawn other subagents.** All orchestration happens in the\nmain thread. Workers and reducers are subagents that operate on files.\n\n## Use Cases\n\n### 1. Parallel Planning\n\nFan out planning task to multiple providers with different strategic biases:\n\n```\nWorkers:\n  - planner-conservative: Low-risk, proven patterns\n  - planner-aggressive: Fast-track, modern patterns\n  - planner-security: Security-first approach\n\nReducer: plan-reducer\nOutput: specs\u002FROADMAP.md\n```\n\nSee: `cookbook\u002Fparallel-planning.md`\n\n### 2. Multi-Implementation\n\nGenerate the same feature with multiple models, pick best:\n\n```\nWorkers:\n  - impl-claude: Claude's implementation\n  - impl-codex: OpenAI's implementation\n  - impl-gemini: Gemini's implementation\n\nReducer: code-reducer\nOutput: src\u002Ffeature\u002Fimplementation.ts\n```\n\nSee: `cookbook\u002Fmulti-impl.md`\n\n### 3. Debug Consensus\n\nGet multiple diagnoses of a bug, verify and select best fix:\n\n```\nWorkers:\n  - debug-claude: Claude's diagnosis\n  - debug-codex: Codex's diagnosis\n  - debug-gemini: Gemini's diagnosis\n\nReducer: debug-reducer\nOutput: Applied fix + documentation\n```\n\nSee: `cookbook\u002Fdebug-consensus.md`\n\n## Available Reducers\n\n| Reducer | Agent Path | Purpose |\n|---------|------------|---------|\n| `plan-reducer` | `agents\u002Forchestration\u002Freducers\u002Fplan-reducer.md` | Consolidate plans |\n| `code-reducer` | `agents\u002Forchestration\u002Freducers\u002Fcode-reducer.md` | Compare\u002Fmerge code |\n| `debug-reducer` | `agents\u002Forchestration\u002Freducers\u002Fdebug-reducer.md` | Verify fixes |\n\n## Provider Integration\n\n### Claude Subagents (via Task tool)\n\n```\nTask(subagent_type=\"Plan\", prompt=\"...\", run_in_background=true)\n```\n\n### External CLI Providers (via spawn skill)\n\n```bash\n# Codex\ncodex -m gpt-5.1-codex -a full-auto \"${PROMPT}\" > output.md\n\n# Gemini\ngemini -m gemini-3-pro \"${PROMPT}\" > output.md\n\n# Cursor\ncursor-agent --mode print \"${PROMPT}\" > output.md\n\n# OpenCode\nopencode --provider anthropic \"${PROMPT}\" > output.md\n```\n\nSee: `skills\u002Fspawn\u002Fagent\u002Fcookbook\u002F` for detailed CLI patterns.\n\n## File Conventions\n\nAll MapReduce operations follow standard file conventions:\n\n| Type | Location | Naming |\n|------|----------|--------|\n| Plan outputs | `specs\u002Fplans\u002F` | `planner-{name}.md` |\n| Code outputs | `implementations\u002F` | `impl-{name}.{ext}` |\n| Debug outputs | `diagnoses\u002F` | `debug-{name}.md` |\n| Consolidated | Specified in prompt | `ROADMAP.md`, `implementation.ts` |\n\nSee: `reference\u002Ffile-conventions.md`\n\n## Scoring Rubrics\n\nEach reducer uses a specific scoring rubric:\n\n- **Plans**: Completeness, Feasibility, Risk, Clarity, Innovation\n- **Code**: Correctness, Readability, Maintainability, Performance, Security\n- **Debug**: Correctness, Minimality, Safety, Clarity, Root Cause\n\nSee: `reference\u002Fscoring-rubrics.md`\n\n## Commands\n\n| Command | Purpose |\n|---------|---------|\n| `\u002Fai-dev-kit:mapreduce` | Full MapReduce workflow |\n| `\u002Fai-dev-kit:map` | Just the fan-out phase |\n| `\u002Fai-dev-kit:reduce` | Just the consolidation phase |\n\n## Example: Full MapReduce\n\n```markdown\n# In main thread:\n\n## Step 1: MAP\n\nLaunch planners in a single message (enables parallelism):\n\nTask(subagent_type=\"Plan\", prompt=\"\"\"\n  Create implementation plan for: User Authentication\n  Write to: specs\u002Fplans\u002Fplanner-conservative.md\n  Strategy: Conservative - proven patterns, minimal risk\n\"\"\", run_in_background=true)\n\nTask(subagent_type=\"Plan\", prompt=\"\"\"\n  Create implementation plan for: User Authentication\n  Write to: specs\u002Fplans\u002Fplanner-aggressive.md\n  Strategy: Aggressive - fast, modern patterns\n\"\"\", run_in_background=true)\n\nBash(\"codex -m gpt-5.1-codex -a full-auto 'Create auth plan' > specs\u002Fplans\u002Fplanner-codex.md\")\n\n## Step 2: COLLECT\n\nTaskOutput(task_id=conservative-id, block=true, timeout=120000)\nTaskOutput(task_id=aggressive-id, block=true, timeout=120000)\n\n# Verify codex output exists\nRead(\"specs\u002Fplans\u002Fplanner-codex.md\")\n\n## Step 3: REDUCE\n\nTask(subagent_type=\"ai-dev-kit:orchestration:plan-reducer\", prompt=\"\"\"\n  Consolidate plans in specs\u002Fplans\u002F*.md\n  Output: specs\u002FROADMAP.md\n  Priority: Security over speed\n\"\"\")\n```\n\n## Cookbook\n\n- `parallel-planning.md`: Multi-provider planning workflows\n- `multi-impl.md`: Code generation with selection\n- `debug-consensus.md`: Multi-diagnosis bug fixing\n\n## Reference\n\n- `scoring-rubrics.md`: Detailed scoring criteria\n- `file-conventions.md`: Output file standards\n\n## Related Skills\n\n- `spawn`: Provider-specific CLI invocation patterns\n- `multi-agent-orchestration`: General multi-agent patterns\n- `research`: Parallel research with synthesis\n","# MapReduce Skill\n\n> **Skill ID**: mapreduce\n> **Purpose**: Fan-out tasks to multiple providers\u002Fagents, then consolidate results\n> **Category**: Orchestration\n\n## Overview\n\nThe MapReduce skill enables parallel task execution across multiple AI providers\nor agent instances, followed by intelligent consolidation of results. This\nproduces higher-quality outputs by leveraging diverse model strengths and\ncross-validating findings.\n\n## Architecture\n\n```\n┌─────────────────────────────────────────────────────────────────────────┐\n│                      MAIN THREAD (Orchestrator)                          │\n│                                                                         │\n│  ┌─────────────────────────────────────────────────────────────────┐    │\n│  │  PHASE 1: MAP (Parallel Fan-Out)                                │    │\n│  │                                                                 │    │\n│  │  Task(worker-1) ──→ output-1.md                                │    │\n│  │  Task(worker-2) ──→ output-2.md                                │    │\n│  │  Task(worker-3) ──→ output-3.md                                │    │\n│  │  bash(codex)   ──→ output-codex.md                             │    │\n│  │  bash(gemini)  ──→ output-gemini.md                            │    │\n│  └─────────────────────────────────────────────────────────────────┘    │\n│                              │                                          │\n│                              ▼                                          │\n│  ┌─────────────────────────────────────────────────────────────────┐    │\n│  │  PHASE 2: COLLECT (Timeout-Based)                               │    │\n│  │                                                                 │    │\n│  │  TaskOutput(worker-1, timeout=120s)                            │    │\n│  │  TaskOutput(worker-2, timeout=120s)                            │    │\n│  │  TaskOutput(worker-3, timeout=120s)                            │    │\n│  │  Verify: output-codex.md, output-gemini.md exist               │    │\n│  └─────────────────────────────────────────────────────────────────┘    │\n│                              │                                          │\n│                              ▼                                          │\n│  ┌─────────────────────────────────────────────────────────────────┐    │\n│  │  PHASE 3: REDUCE (Consolidation)                                │    │\n│  │                                                                 │    │\n│  │  Task(reducer) ──→ reads all outputs ──→ consolidated.md       │    │\n│  └─────────────────────────────────────────────────────────────────┘    │\n└─────────────────────────────────────────────────────────────────────────┘\n```\n\n## Key Constraint\n\n**Subagents cannot spawn other subagents.** All orchestration happens in the\nmain thread. Workers and reducers are subagents that operate on files.\n\n## Use Cases\n\n### 1. Parallel Planning\n\nFan out planning task to multiple providers with different strategic biases:\n\n```\nWorkers:\n  - planner-conservative: Low-risk, proven patterns\n  - planner-aggressive: Fast-track, modern patterns\n  - planner-security: Security-first approach\n\nReducer: plan-reducer\nOutput: specs\u002FROADMAP.md\n```\n\nSee: `cookbook\u002Fparallel-planning.md`\n\n### 2. Multi-Implementation\n\nGenerate the same feature with multiple models, pick best:\n\n```\nWorkers:\n  - impl-claude: Claude's implementation\n  - impl-codex: OpenAI's implementation\n  - impl-gemini: Gemini's implementation\n\nReducer: code-reducer\nOutput: src\u002Ffeature\u002Fimplementation.ts\n```\n\nSee: `cookbook\u002Fmulti-impl.md`\n\n### 3. Debug Consensus\n\nGet multiple diagnoses of a bug, verify and select best fix:\n\n```\nWorkers:\n  - debug-claude: Claude's diagnosis\n  - debug-codex: Codex's diagnosis\n  - debug-gemini: Gemini's diagnosis\n\nReducer: debug-reducer\nOutput: Applied fix + documentation\n```\n\nSee: `cookbook\u002Fdebug-consensus.md`\n\n## Available Reducers\n\n| Reducer | Agent Path | Purpose |\n|---------|------------|---------|\n| `plan-reducer` | `agents\u002Forchestration\u002Freducers\u002Fplan-reducer.md` | Consolidate plans |\n| `code-reducer` | `agents\u002Forchestration\u002Freducers\u002Fcode-reducer.md` | Compare\u002Fmerge code |\n| `debug-reducer` | `agents\u002Forchestration\u002Freducers\u002Fdebug-reducer.md` | Verify fixes |\n\n## Provider Integration\n\n### Claude Subagents (via Task tool)\n\n```\nTask(subagent_type=\"Plan\", prompt=\"...\", run_in_background=true)\n```\n\n### External CLI Providers (via spawn skill)\n\n```bash\n# Codex\ncodex -m gpt-5.1-codex -a full-auto \"${PROMPT}\" > output.md\n\n# Gemini\ngemini -m gemini-3-pro \"${PROMPT}\" > output.md\n\n# Cursor\ncursor-agent --mode print \"${PROMPT}\" > output.md\n\n# OpenCode\nopencode --provider anthropic \"${PROMPT}\" > output.md\n```\n\nSee: `skills\u002Fspawn\u002Fagent\u002Fcookbook\u002F` for detailed CLI patterns.\n\n## File Conventions\n\nAll MapReduce operations follow standard file conventions:\n\n| Type | Location | Naming |\n|------|----------|--------|\n| Plan outputs | `specs\u002Fplans\u002F` | `planner-{name}.md` |\n| Code outputs | `implementations\u002F` | `impl-{name}.{ext}` |\n| Debug outputs | `diagnoses\u002F` | `debug-{name}.md` |\n| Consolidated | Specified in prompt | `ROADMAP.md`, `implementation.ts` |\n\nSee: `reference\u002Ffile-conventions.md`\n\n## Scoring Rubrics\n\nEach reducer uses a specific scoring rubric:\n\n- **Plans**: Completeness, Feasibility, Risk, Clarity, Innovation\n- **Code**: Correctness, Readability, Maintainability, Performance, Security\n- **Debug**: Correctness, Minimality, Safety, Clarity, Root Cause\n\nSee: `reference\u002Fscoring-rubrics.md`\n\n## Commands\n\n| Command | Purpose |\n|---------|---------|\n| `\u002Fai-dev-kit:mapreduce` | Full MapReduce workflow |\n| `\u002Fai-dev-kit:map` | Just the fan-out phase |\n| `\u002Fai-dev-kit:reduce` | Just the consolidation phase |\n\n## Example: Full MapReduce\n\n```markdown\n# In main thread:\n\n## Step 1: MAP\n\nLaunch planners in a single message (enables parallelism):\n\nTask(subagent_type=\"Plan\", prompt=\"\"\"\n  Create implementation plan for: User Authentication\n  Write to: specs\u002Fplans\u002Fplanner-conservative.md\n  Strategy: Conservative - proven patterns, minimal risk\n\"\"\", run_in_background=true)\n\nTask(subagent_type=\"Plan\", prompt=\"\"\"\n  Create implementation plan for: User Authentication\n  Write to: specs\u002Fplans\u002Fplanner-aggressive.md\n  Strategy: Aggressive - fast, modern patterns\n\"\"\", run_in_background=true)\n\nBash(\"codex -m gpt-5.1-codex -a full-auto 'Create auth plan' > specs\u002Fplans\u002Fplanner-codex.md\")\n\n## Step 2: COLLECT\n\nTaskOutput(task_id=conservative-id, block=true, timeout=120000)\nTaskOutput(task_id=aggressive-id, block=true, timeout=120000)\n\n# Verify codex output exists\nRead(\"specs\u002Fplans\u002Fplanner-codex.md\")\n\n## Step 3: REDUCE\n\nTask(subagent_type=\"ai-dev-kit:orchestration:plan-reducer\", prompt=\"\"\"\n  Consolidate plans in specs\u002Fplans\u002F*.md\n  Output: specs\u002FROADMAP.md\n  Priority: Security over speed\n\"\"\")\n```\n\n## Cookbook\n\n- `parallel-planning.md`: Multi-provider planning workflows\n- `multi-impl.md`: Code generation with selection\n- `debug-consensus.md`: Multi-diagnosis bug fixing\n\n## Reference\n\n- `scoring-rubrics.md`: Detailed scoring criteria\n- `file-conventions.md`: Output file standards\n\n## Related Skills\n\n- `spawn`: Provider-specific CLI invocation patterns\n- `multi-agent-orchestration`: General multi-agent patterns\n- 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