skill-creator

Name: skill-creator
Availability: InStock
Author: logesh

Create and improve skills using Python and Markdown-based SKILL.md definitions.

Updated Apr 18, 2026

Search Tags:#skill creation #AI development #skill improvement #feedback processing #skill measurement

Ready to run

npx skills add https://github.com/logeshv586-code/AIproductfactory --skill skill-creator-logeshv586-code

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/logeshv586-code/AIproductfactory/tree/main/skills/skill-creator
Command: npx skills add https://github.com/logeshv586-code/AIproductfactory --skill skill-creator-logeshv586-code

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires subagents, eval-viewer, glm, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of creating, improving, and measuring skill performance, enabling users to efficiently develop and optimize their AI skills.

Core Features & Use Cases

Skill Creation: Assist users in creating new skills from scratch, capturing intent, and writing SKILL.md files.
Skill Improvement: Help users modify existing skills based on feedback and benchmarking results.
Skill Measurement: Provide tools for evaluating skill performance, including test case execution and comparison against baselines.
Use Case: A user wants to create a skill for processing customer feedback. This Skill guides them through capturing intent, designing the skill, writing the SKILL.md file, and running test cases to ensure accuracy.

Quick Start

Use the skill-creator skill to help me create a skill for processing customer feedback. We'll start by defining the skill's purpose and expected output.

Frequently Asked Questions about skill-creator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create a new AI skill from scratch using Python and Markdown?▼

To create a new AI skill from scratch, you define the skill's purpose and expected output, then the system generates a SKILL.md file using Python and Markdown definitions. This interactive process captures intent and structures the skill configuration.

How can I improve an existing AI skill based on user feedback?▼

You can improve an existing AI skill by processing user feedback and benchmarking results. The system iteratively modifies skill definitions, runs test cases, and compares performance against baselines to optimize behavior.

What is the best way to measure AI skill performance during development?▼

The best way to measure AI skill performance is by executing test cases and comparing results against baselines. The system uses subagents for parallel evaluation, providing detailed metrics on skill accuracy and behavior.

Do I need a specific Python environment to run skill creation and evaluation workflows?▼

Yes, you need a Python environment with specified libraries and tools to run skill creation and evaluation workflows. The system depends on subagents, eval-viewer, and glm components to facilitate parallel execution and interactive design.

Can I use subagents for parallel execution when testing AI skills?▼

Yes, you can use subagents for parallel execution when testing AI skills. The system utilizes subagents to run concurrent evaluations, enabling efficient measurement of skill performance across multiple test cases.

What are the limitations of using Markdown-based definitions for skill improvement?▼

Markdown-based definitions for skill improvement rely heavily on a specific Python environment and dependencies like subagents and glm. Limitations include potential bottlenecks in interactive design if the required libraries are not properly configured.