core-build

name	core-build
description	This skill should be used when designing CrewAI crews with YAML-first architecture. Use it to create crews, configure agents and tasks, select processes, and generate validation-ready outputs.
version	3.1.0
tags	["crewai","agents","tasks","yaml","configuration"]

Core Build

Comprehensive skill for building CrewAI crews with YAML-first architecture.

When To Use

• Create new crew projects
• Configure agents and tasks
• Define process workflows
• Bootstrap crew infrastructure
• Validate crew configurations
• Generate boilerplate code

Quick Start Workflow

1. Scaffold New Project

python scripts/scaffold_crew.py my_crew

2. Configure Agents and Tasks

Edit config/agents.yaml and config/tasks.yaml using the templates.

3. Validate Configuration

python scripts/validate_crew.py my_crew

4. Execute Crew

python my_crew/main.py "your input here"

Detailed Workflow

Design Phase

1. Define Purpose: Clarify what the crew should accomplish
2. Identify Agents: Determine roles needed (researcher, analyst, writer, etc.)
3. Map Tasks: Break work into single-responsibility tasks
4. Plan Dependencies: Establish context flow between tasks

Configuration Phase

1. Create agents.yaml: Define agent roles, goals, and backstories
2. Create tasks.yaml: Define task descriptions and expected outputs
3. Set Context: Link tasks using context dependencies
4. Configure Tools: Assign appropriate tools to agents

Validation Phase

Run validation to catch errors early:

python scripts/validate_crew.py ./my_crew

Validates:

• YAML syntax and structure
• Required field presence
• Agent reference integrity
• Task context dependencies
• Circular dependency detection
• Type checking

Execution Phase

Use the scaffolded main.py or integrate into your application:

from crew import MyCrewCrew

inputs = {"topic": "your input"}
result = MyCrewCrew().crew().kickoff(inputs=inputs)

Process Selection Guide

Sequential Process

• Tasks execute in order
• Each task can access previous task outputs
• Simple and predictable

Use for: Data pipelines, content creation, research workflows

from crewai import Process

crew = Crew(
    process=Process.sequential,
    # ...
)

Hierarchical Process

• Manager coordinates task execution
• Dynamic task assignment
• Built-in quality review

Use for: Complex projects, quality-critical work, multi-agent collaboration

from crewai import Process

crew = Crew(
    process=Process.hierarchical,
    manager_llm="gpt-4",
    # ...
)

Common Patterns

Research → Analysis → Report

# agents.yaml
researcher:
  role: Research Specialist
  goal: Gather comprehensive information

analyst:
  role: Data Analyst
  goal: Extract insights and patterns

# tasks.yaml
research_task:
  description: Research {topic}
  agent: researcher
  
analysis_task:
  description: Analyze findings
  agent: analyst
  context:
    - research_task

Multi-Agent Collaboration

Specialized agents working on different aspects simultaneously.

Review Loop Pattern

Iterative improvement with feedback cycles and revisions.

See references/guides/patterns.md for complete pattern library.

Decorator Reference

@CrewBase

Marks the class as a CrewBase configuration class.

@CrewBase
class MyCrew:
    agents_config = "config/agents.yaml"
    tasks_config = "config/tasks.yaml"

@agent

Defines an agent method that returns an Agent instance.

@agent
def researcher(self) -> Agent:
    return Agent(config=self.agents_config['researcher'])

@task

Defines a task method that returns a Task instance.

@task
def research_task(self) -> Task:
    return Task(config=self.tasks_config['research_task'])

@crew

Defines the crew method that returns the Crew instance.

@crew
def crew(self) -> Crew:
    return Crew(agents=self.agents, tasks=self.tasks)

@before_kickoff

Runs before crew execution starts.

@before_kickoff
def before_kickoff_function(self, inputs):
    print(f"Starting with inputs: {inputs}")
    return inputs

@after_kickoff

Runs after crew execution completes.

@after_kickoff
def after_kickoff_function(self, result):
    print(f"Completed with result: {result}")
    return result

Tool Usage

scaffold_crew.py

Scaffolds a new crew project from templates.

# Create in current directory
python scripts/scaffold_crew.py my_crew

# Create in specific directory
python scripts/scaffold_crew.py my_crew --path ./crews

validate_crew.py

Validates crew configuration files.

# Validate a crew
python scripts/validate_crew.py ./my_crew

# With detailed output
python scripts/validate_crew.py ./my_crew --verbose

generate_config.py

Interactive configuration generator.

# Generate interactively
python scripts/generate_config.py

# Choose agent, task, or full crew setup

Asset Templates

Starter Project

• assets/starter/ - Complete working crew template
  • config/agents.yaml - Researcher and analyst agents
  • config/tasks.yaml - Research and analysis tasks
  • crew.py - Full CrewBase class
  • main.py - CLI entry point
  • .env.example - Environment template
  • README.md - Project documentation

Configuration Templates

• assets/templates/agents-basic.yaml - Starter agent configuration
• assets/templates/agents-advanced.yaml - All agent parameters
• assets/templates/tasks-basic.yaml - Starter task configuration
• assets/templates/tasks-advanced.yaml - All task parameters

Reference Documentation

API Documentation

• references/api/agents.md - Complete agent attribute reference
• references/api/tasks.md - Complete task attribute reference
• references/api/crews.md - Complete crew attribute reference

Guides

• references/guides/best-practices.md - Design principles and guidelines
• references/guides/patterns.md - Common crew architecture patterns

External Resources

• references/external.md - Official documentation links

Troubleshooting

Common Issues

Missing required fields

Error: Agent 'name' missing required field 'role'

Solution: Add all required fields (role, goal, backstory)

Agent not found

Error: Task references unknown agent 'wrong_name'

Solution: Ensure agent name in tasks.yaml matches agents.yaml

Circular dependency

Error: Circular dependency detected

Solution: Remove the circular reference in task context

YAML syntax error

Error: YAML parse error at line 5

Solution: Check indentation (use 2 spaces) and syntax

Validation Always Passes First

Always run validate_crew.py before execution to catch errors early.

Debugging Tips

1. Enable verbose mode: verbose: true
2. Check context dependencies are correct
3. Validate agent names match exactly
4. Use markdown formatting for readability
5. Test with simple inputs first

Mastery Steps

1. Study Templates: Review assets/starter/ to understand structure
2. Configure Agents: Use references/api/agents.md for options
3. Define Tasks: Use references/api/tasks.md for configuration
4. Validate: Run scripts/validate_crew.py to check for errors
5. Test: Execute with sample inputs and refine
6. Iterate: Use patterns from references/guides/patterns.md for complex crews

File Structure

core-build/
├── SKILL.md                           # This file
├── assets/
│   ├── starter/                       # Complete working project
│   │   ├── config/
│   │   │   ├── agents.yaml           # Starter agent config
│   │   │   └── tasks.yaml            # Starter task config
│   │   ├── crew.py                   # CrewBase implementation
│   │   ├── main.py                   # CLI entry point
│   │   ├── .env.example              # Environment template
│   │   └── README.md                 # Project documentation
│   └── templates/                     # Standalone templates
│       ├── agents-basic.yaml         # Basic agent config
│       ├── agents-advanced.yaml      # Advanced agent reference
│       ├── tasks-basic.yaml          # Basic task config
│       └── tasks-advanced.yaml       # Advanced task reference
├── references/
│   ├── api/                           # API documentation
│   │   ├── agents.md                 # Agent attributes
│   │   ├── tasks.md                  # Task attributes
│   │   └── crews.md                  # Crew attributes
│   ├── guides/                        # How-to guides
│   │   ├── best-practices.md         # Best practices
│   │   └── patterns.md               # Architecture patterns
│   └── external.md                   # Official docs links
└── scripts/                           # Executable tools
    ├── scaffold_crew.py              # Project scaffolding
    ├── validate_crew.py              # Configuration validation
    └── generate_config.py            # Interactive config generator

Coding-Agent Guidelines

• Design crews with contracts over prose; keep prompts out of inline Python constructors
• Enforce single-purpose tasks with explicit expected outputs and context dependencies
• Make process choice explicit and justified for reproducibility
• Produce artifacts that are directly reusable by downstream Flows
• Always validate configurations before execution
• Use templates as starting points, customize for specific needs
• Follow naming conventions: snake_case for files, descriptive names for agents/tasks
• Document assumptions and constraints in task descriptions
• Test with edge cases and validate error handling