skill-creator

Automate Claude skill creation, evaluation, benchmarking, and optimization.

1|Updated Mar 21, 2026
One-click install
npx skills add https://github.com/dashkan/pivox-server --skill skill-creator-dashkan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/dashkan/pivox-server/tree/main/.agents/skills/skill-creator
Command: npx skills add https://github.com/dashkan/pivox-server --skill skill-creator-dashkan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anthropic, PyYAML.

What problem does it solve?

This Skill provides an end-to-end workflow for creating new Claude skills, evaluating their effectiveness, benchmarking performance, and iterating on descriptions to improve triggering accuracy.

Core Features & Use Cases

  • Orchestrates the full lifecycle of skill creation, evaluation, and optimization.
  • Supports iterative eval loops, benchmark analysis, and automatic description improvement to increase triggering accuracy.
  • Integrates with the eval framework, exporters, and optional bundled resources to standardize skill packaging and testing.

Quick Start

Draft your initial skill in SKILL.md, then run the evaluation loop to begin iterative improvement.

Frequently Asked Questions about skill-creator

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

FAQPage Schema
How do I automate Claude skill creation and evaluation end-to-end?▼

You can automate Claude skill creation and evaluation by drafting an initial SKILL.md file and running an iterative evaluation loop to trigger automated benchmarking and description refinement.

What is an iterative evaluation loop for improving skill triggering accuracy?▼

An iterative evaluation loop is a controlled testing process that benchmarks skill performance and automatically refines descriptions to increase triggering accuracy and validate effectiveness reproducibly.

Does the skill-creator workflow require specific Python dependencies?▼

Yes, the skill-creator workflow requires the Anthropic and PyYAML Python dependencies to orchestrate skill creation, evaluation, and standardized packaging through its CLI automation.

How do I benchmark and optimize existing Claude skills?▼

You can benchmark and optimize existing Claude skills by applying controlled evaluations and benchmark analysis to validate triggering accuracy, then iterating on descriptions to improve performance.

Can I standardize skill packaging and testing for reproducible outputs?▼

Yes, you can standardize skill packaging and testing for reproducible outputs by integrating the evaluation framework and exporters to generate clear, verifiable results for reviewers.

What are the limitations when using automation for skill refinement?▼

Skill refinement automation relies on controlled evals and benchmark analysis, meaning its optimization is constrained by the quality of the initial SKILL.md draft and the evaluation parameters defined by the user.