Expert Claude Code Skills architect that helps create, edit, and convert sub-agents to skills using CLI tools, Node.js scripting, and best practices.
git clone https://github.com/metaskills/skill-builder.gitskill-builder helps developers architect Claude Code skills from scratch, refine existing skills, and convert sub-agent configurations to skill format. It provides interactive guidance for designing skill workflows, writing SKILL.md files with proper metadata, organizing supporting files with intention-revealing names, and leveraging CLI tools and Node.js scripting. The skill emphasizes modern tooling patterns, progressive disclosure for documentation, and adherence to official Claude Code agent skills best practices and conventions.
Use skill-builder when you need to create a new skill, refine an existing one, or convert a sub-agent configuration. Provide details about the task or workflow, use cases, and scope (personal or project-specific). The skill guides you through interactive requirements gathering, design decisions, implementation, and validation.
Creating new Claude Code skills with proper structure and metadata
Editing existing skills to improve descriptions and organization
Converting Claude Code sub-agents to reusable skills
Designing skill workflows with CLI and Node.js integration
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/metaskills/skill-builderCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a Claude Code Agent Skills Builder. I want to create a new skill for [SPECIFIC TASK]. The skill should be able to [INPUT TYPE] and [OUTPUT TYPE]. The skill should be named [SKILL NAME]. Provide the skill definition in JSON format.
```json
{
"name": "Data Analyzer",
"description": "Analyzes CSV data and generates insights",
"inputs": [
{
"name": "file_path",
"type": "string",
"description": "Path to the CSV file"
}
],
"outputs": [
{
"name": "insights",
"type": "string",
"description": "Generated insights from the data"
}
],
"code": "def analyze_data(file_path):\n import pandas as pd\n data = pd.read_csv(file_path)\n insights = data.describe()\n return str(insights)\n"
}
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