taskmaster
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.
Best use case
taskmaster is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using taskmaster should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/taskmaster/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How taskmaster Compares
| Feature / Agent | taskmaster | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 ```markdown TaskMaster: Research PDF processing libraries - Budget: $2.00 - Priority: medium - Deadline: 2 hours ``` ### 2. Complex Project Breakdown ```markdown 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: ```markdown TaskMaster: Debug complex algorithm [FORCE: Opus] ``` ### Parallel Execution Run multiple tasks simultaneously: ```markdown TaskMaster: Multi-research project - Task A: Library comparison - Task B: Performance benchmarks - Task C: Security analysis [PARALLEL: true] ``` ### Budget Controls Set spending limits: ```markdown 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](references/model-selection-rules.md) for detailed complexity guidelines - **Task Templates**: See [references/task-templates.md](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.
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