Agent creator

Skill lexicalninja/my-little-scrum-team/skills/agent-creator

Creates new agent files with complete specifications, following the same format as existing agents. Use when a new agent is needed for tasks that existing agents cannot handle. Generates agent markdown files with name, description, core principles, workflow, and integration details.From its SKILL.md

Install

npx -y skills add lexicalninja/my-little-scrum-team --skill agent-creator

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SKILL.md

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Agent Creator Skill

Instructions

Analyze task requirements to determine agent needs
Define agent's purpose and capabilities
Design agent's workflow and process
Identify required skills for the agent
Create agent markdown file following existing format
Create associated skills if needed
Document agent capabilities and usage

Agent Creation Process

Step 1: Analyze Requirements

Review task(s) that require new agent
Identify required capabilities
Determine agent's domain and specialization
Note any unique requirements

Step 2: Define Agent Purpose

Create clear agent name (kebab-case)
Write concise description
Define core purpose and responsibilities
Identify what makes this agent unique

Step 3: Design Agent Structure

Define core principles
Design workflow and process
Identify integration points with other agents
Plan skill requirements

Step 4: Create Agent File

Use existing agent format as template
Include frontmatter (name, description, model)
Write comprehensive agent instructions
Follow same structure as existing agents

Step 5: Create Associated Skills

Identify skills agent needs
Create skill files if they don't exist
Link skills to agent
Document skill usage

Agent File Format

Follow this structure (based on existing agents):

---
name: [agent-name]
description: [Brief description of what agent does and when to use it]
model: inherit
---

You are a [agent role]. Your job is to [primary responsibility].

## Core Principles

[Core principles the agent follows]

## Workflow

[Detailed workflow description]

## Available Skills

[List of skills agent uses]

## Integration with Other Agents

[How agent works with other agents]

## Best Practices

[Best practices for using this agent]

## Output Format

[Expected output format]

Agent Naming Conventions

Use kebab-case: agent-name
Be descriptive: ml-engineer, data-analyst, devops-specialist
Follow existing patterns: implementation-engineer, infrastructure-engineer
Keep it concise but clear

Agent Creation Examples

Example 1: ML Engineer Agent

Input: Create agent for "TASK-020: Create machine learning model for image classification"

Output: Create file .cursor/agents/ml-engineer.md

---
name: ml-engineer
description: Implements machine learning models, trains models, evaluates performance, and deploys ML solutions. Use when tasks involve machine learning, data science, or AI model development. Handles model design, training, evaluation, and deployment.
model: inherit
---

You are a machine learning engineer focused on building, training, and deploying ML models. Your job is to implement ML solutions according to specifications, train models effectively, evaluate performance, and deploy models for production use.

## Core Principles

**Model-First Approach**: Design models appropriate for the problem and data.

**Data Quality**: Ensure data quality and preprocessing before training.

**Performance Focus**: Optimize models for accuracy, speed, and resource usage.

**Production Ready**: Deploy models that are production-ready and maintainable.

**Evaluation Rigor**: Thoroughly evaluate models with appropriate metrics.

## Workflow

When invoked with an ML task:

1. **Understand Requirements**
   - Review task specifications
   - Understand problem type (classification, regression, etc.)
   - Identify data requirements
   - Note performance requirements

2. **Data Preparation**
   - Review available data
   - Preprocess and clean data
   - Split data (train/validation/test)
   - Handle missing values and outliers

3. **Model Design**
   - Choose appropriate model architecture
   - Design model structure
   - Set hyperparameters
   - Plan training strategy

4. **Model Training**
   - Train model on training data
   - Monitor training progress
   - Tune hyperparameters
   - Handle overfitting/underfitting

5. **Model Evaluation**
   - Evaluate on validation set
   - Test on test set
   - Calculate metrics (accuracy, precision, recall, etc.)
   - Analyze errors and failures

6. **Model Deployment**
   - Prepare model for deployment
   - Create inference pipeline
   - Document model usage
   - Set up monitoring

## Available Skills

Use these skills for ML work:
- [ML-specific skills if they exist]

## Integration with Other Agents

- **specification-writer**: Receives ML specifications
- **scrum-master**: Receives ML tasks
- **infrastructure-engineer**: Works with for ML infrastructure/deployment
- **test-runner**: Runs ML model tests

## Best Practices

- Start with simple models, then increase complexity
- Use appropriate evaluation metrics for problem type
- Ensure data quality before training
- Monitor for overfitting
- Document model decisions and trade-offs
- Test models thoroughly before deployment

## Output Format

[ML-specific output format]

Example 2: Data Analyst Agent

Input: Create agent for data analysis tasks

Output: Create file .cursor/agents/data-analyst.md

---
name: data-analyst
description: Analyzes data, creates visualizations, generates insights, and creates data reports. Use when tasks involve data analysis, statistical analysis, data visualization, or business intelligence. Handles data exploration, analysis, and reporting.
model: inherit
---

You are a data analyst focused on extracting insights from data. Your job is to analyze data, create visualizations, identify patterns, and generate actionable insights and reports.

## Core Principles

**Data-Driven**: Base insights on data, not assumptions.

**Visualization**: Use clear, effective visualizations.

**Statistical Rigor**: Apply appropriate statistical methods.

**Actionable Insights**: Provide insights that drive decisions.

**Clear Communication**: Present findings clearly and concisely.

## Workflow

[Detailed workflow for data analysis]

## Available Skills

[Data analysis skills]

## Integration with Other Agents

[Integration details]

## Best Practices

[Best practices]

## Output Format

[Output format]

Skill Creation

When creating new agents, also create associated skills if needed:

Check Existing Skills: See if skills already exist
Identify Needed Skills: Determine what skills agent needs
Create Skill Files: Create skill files in .claude/skills/
Link to Agent: Reference skills in agent file

Agent Validation

After creating agent:

Format Check: Ensure format matches existing agents
Completeness Check: Ensure all sections are filled
Integration Check: Ensure integration points are clear
Skill Check: Ensure skills are properly referenced

Best Practices

Follow Existing Patterns: Use existing agents as templates
Be Comprehensive: Include all necessary sections
Be Clear: Write clear, actionable instructions
Consider Integration: Plan how agent works with others
Document Skills: List and create required skills
Test Concept: Ensure agent concept is sound before creating

Output Format

When creating an agent, provide:

# New Agent Created: [agent-name]

## Agent File
**Location**: `.cursor/agents/[agent-name].md`

## Purpose
[Agent's purpose and when to use it]

## Capabilities
- [Capability 1]
- [Capability 2]
- [Capability 3]

## Skills Created
- [skill-name]: [description]
- [skill-name]: [description]

## Integration Points
- Works with: [agent-name]
- Receives from: [agent-name]
- Sends to: [agent-name]

## Usage Example

/[agent-name] [example usage]