什么是 TaskMaster？

TaskMaster 是一个先进的项目管理和分派框架，旨在将高层目标转化为可管理的执行工作流。作为 Openclaw Skills 生态系统中的编排层，它能够智能地将任务分配给最合适的 AI 模型——从用于数据提取的轻量级模型到用于架构决策的高推理模型。这确保了复杂项目能够高效完成，而不会在 Token 成本上过度支出。

该技能专注于自主子代理管理，允许用户生成独立的进程，并行处理项目的特定部分。TaskMaster 充当中央大脑，监控进度、管理预算，并将零散的输出整合为高质量的交付成果。

下载入口:https://github.com/openclaw/skills/tree/main/skills/jlwrow/taskmaster

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

TaskMaster 应用场景

• 编排多步骤开发工作流，例如构建全栈 MVP。
• 通过运行并行搜索和分析任务进行深度市场调研。
• 管理大规模 AI 自动化项目的 Token 预算。
• 将数据格式化等常规任务分派给低成本模型，同时将复杂推理留给高级模型。
• 在生产环境中自动执行失败任务重试和升级路径。

1. 任务分流：TaskMaster 分析项目需求以确定复杂性和具体技术需求。
2. 模型选择：根据预定义的复杂度规则自动分配最适合的模型（Haiku、Sonnet 或 Opus）。
3. 子代理编排：系统生成具有特定限制和工具访问权限的独立子代理。
4. 执行与跟踪：执行任务（可选并行），同时系统跟踪实时 Token 消耗和状态。
5. 结果聚合：TaskMaster 收集所有子代理的结果并将其编译成最终报告或代码库。

TaskMaster 配置指南

要开始使用 TaskMaster，请确保您的 Openclaw Skills 环境已激活。按照以下步骤初始化分派引擎：

# 安装分派引擎所需的依赖项
pip install -r requirements.txt

# 在您的代理环境中启用 TaskMaster 技能
openclaw install taskmaster

# 配置您的模型 API 密钥和默认预算限制
cp config.example.yaml config.yaml

TaskMaster 数据架构与分类体系

TaskMaster 维持一个结构化的层次结构来管理项目元数据和子代理输出。数据组织方式如下：

name: taskmaster
description: Project manager and task delegation system. Use when you need to break down complex work into smaller tasks, assign appropriate AI models based on complexity, spawn sub-agents for parallel execution, track progress, and manage token budgets. Ideal for research projects, multi-step workflows, or when you want to delegate routine tasks to cheaper models while handling complex coordination yourself.

TaskMaster: AI Project Manager & Task Delegation

Transform complex projects into managed workflows with smart model selection and sub-agent orchestration.

Core Capabilities

?? Smart Task Triage

• Analyze complexity → assign appropriate model (Haiku/Sonnet/Opus)
• Break large projects into smaller, manageable tasks
• Prevent over-engineering (don't use Opus for simple web searches)

?? Sub-Agent Orchestration

• Spawn isolated sub-agents with specific model constraints
• Run tasks in parallel for faster completion
• Consolidate results into coherent deliverables

?? Budget Management

• Track token costs per task and total project
• Set budget limits to prevent runaway spending
• Optimize model selection for cost-efficiency

?? Progress Tracking

• Real-time status of all active tasks
• Failed task retry with escalation
• Final deliverable compilation

Quick Start

1. Basic Task Delegation

TaskMaster: Research PDF processing libraries
- Budget: $2.00
- Priority: medium
- Deadline: 2 hours

2. Complex Project Breakdown

TaskMaster: Build recipe app MVP
- Components: UI mockup, backend API, data schema, deployment
- Budget: $15.00
- Timeline: 1 week
- Auto-assign models based on complexity

Model Selection Rules

Haiku ($0.25/$1.25) - Simple, repetitive tasks:

• Web searches & summarization
• Data formatting & extraction
• Basic file operations
• Status checks & monitoring

Sonnet ($3/$15) - Most development work:

• Research & analysis
• Code writing & debugging
• Documentation creation
• Technical design

Opus ($15/$75) - Complex reasoning:

• Architecture decisions
• Creative problem-solving
• Code reviews & optimization
• Strategic planning

Advanced Usage

Custom Model Assignment

Override automatic selection when you know better:

TaskMaster: Debug complex algorithm [FORCE: Opus]

Parallel Execution

Run multiple tasks simultaneously:

TaskMaster: Multi-research project
- Task A: Library comparison
- Task B: Performance benchmarks  
- Task C: Security analysis
[PARALLEL: true]

Budget Controls

Set spending limits:

TaskMaster: Market research
- Max budget: $5.00
- Escalate if >$3.00 spent
- Stop if any single task >$1.00

Key Resources

• Model Selection: See references/model-selection-rules.md for detailed complexity guidelines
• Task Templates: See references/task-templates.md for common task patterns
• Delegation Engine: Uses scripts/delegate_task.py for core orchestration logic

Implementation Notes

Sessions Management: Each sub-agent gets isolated session with specific model constraints. No cross-talk unless explicitly designed.

Error Handling: Failed tasks automatically retry once on Sonnet, then escalate to human review.

Result Aggregation: TaskMaster compiles all sub-agent results into a single, coherent deliverable for the user.

Token Tracking: Real-time cost monitoring with alerts when approaching budget limits.