Task Decomposer & Skill Generator

A powerful skill that helps decompose complex user requests into executable subtasks, identify required capabilities, search for existing skills from the open skills ecosystem, and automatically create new skills when no existing solution is available.

Features

Task Decomposition: Breaks down complex requests into atomic, executable subtasks
Capability Identification: Maps tasks to a universal capability taxonomy
Skill Search: Searches the skills.sh ecosystem for existing solutions
Gap Analysis: Identifies tasks without matching skills
Skill Creation: Generates new skills when no existing solution is found
Execution Planning: Creates structured execution plans with dependencies

Installation

npx skills add https://github.com/clawdbot-skills/task-decomposer -g -y

Core Workflow

User Request → Task Decomposition → Capability Identification → Skill Search → Gap Analysis → Skill Creation → Execution Plan

Universal Capability Types

Capability	Description
`browser_automation`	Web navigation, interaction, scraping
`web_search`	Internet search and information retrieval
`api_integration`	Third-party API communication
`data_extraction`	Parse and extract structured data
`data_transformation`	Convert, clean, transform data
`content_generation`	Create text, images, or other content
`file_operations`	Read, write, manipulate files
`message_delivery`	Send notifications or messages
`scheduling`	Time-based task execution
`authentication`	Identity and access management
`database_operations`	Database CRUD operations
`code_execution`	Run scripts or programs
`version_control`	Git and code repository operations
`testing`	Automated testing and QA
`deployment`	Application deployment and CI/CD
`monitoring`	System and application monitoring

Usage Examples

Example 1: Workflow Automation

User Request:

Create a workflow that monitors GitHub issues, summarizes new issues, and posts notifications to Discord

Decomposition Result:

Monitor GitHub repository for new issues (api_integration)
Extract issue content and metadata (built-in)
Generate issue summary (built-in LLM)
Send notification to Discord (message_delivery)
Configure webhook or polling trigger (built-in)

Example 2: Data Pipeline

User Request:

Search for AI research papers, download PDFs, extract key findings, and save to Notion

Decomposition Result:

Search for AI research papers (web_search)
Download PDF files (browser_automation)
Extract text from PDFs (data_extraction)
Generate summaries of key findings (built-in LLM)
Save to Notion database (api_integration)

Task Decomposition Principles

Atomicity: Each subtask should be the minimal executable unit
Independence: Minimize dependencies between tasks
Verifiability: Each task should have a clear verification method
Reusability: Prefer creating general-purpose skills
Single Responsibility: Each task should do one thing well

Best Practices

Always search skills.sh before creating new skills
Use specific search terms combining capability keywords with domain terms
Leverage built-in capabilities for things the agent can do natively
Design new skills to be general-purpose when possible
Document new skills thoroughly with clear usage instructions
Verify skill installation before executing tasks
Include fallback strategies in execution plans

# Search the skills ecosystem
npx skills find <query>
 
# Install a discovered skill
npx skills add <owner/repo@skill> -g -y
 
# Initialize a new skill
npx skills init <skill-name>

License

MIT