skill-creator

Design, test, and refine GLM-based skills with structured SKILL.md files.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/rcstrue/php_payroll --skill skill-creator-rcstrue
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/rcstrue/php_payroll/tree/main/php_payroll/skills/skill-creator
Command: npx skills add https://github.com/rcstrue/php_payroll --skill skill-creator-rcstrue

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyYAML, and includes scripts (resource) components.

What problem does it solve?

The Skill Creator provides a repeatable, documentation-driven workflow to design, test, and refine GLM-based skills, reducing guesswork and increasing reliability.

Core Features & Use Cases

  • Structured SKILL.md authoring: Encourages a consistent frontmatter and rich body that captures intent, scope, and usage.
  • End-to-end evaluation loop: Orchestrates user interviews, test prompts, trigger evaluations, and iterative rewrites.
  • Benchmark-ready outputs: Produces eval prompts, results, and dashboards to compare progress across iterations.
  • Guided packaging and distribution: Includes scripts to validate, package, and share skills across environments.

Quick Start

Draft a new skill concept, write SKILL.md, add an initial eval set, and run the loop to begin iterating.

Frequently Asked Questions about skill-creator

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

FAQPage Schema
How do I create and test GLM skills with a structured workflow?

To create and test GLM skills, define trigger conditions, success criteria, and required inputs, then iterate using evals and benchmarks to optimize performance and produce a ready-to-package skill.

What is the best way to design a repeatable GLM skill authoring process?

The best way to design a repeatable GLM skill authoring process is using a documentation-driven workflow that translates user intent into structured SKILL.md files with consistent frontmatter and rich body content.

How does evaluation-driven iteration work for GLM skills?

Evaluation-driven iteration works by orchestrating user interviews, running test prompts, evaluating triggers, and performing iterative rewrites to compare progress across benchmark dashboards.

Do I need PyYAML to package and distribute GLM skills?

Yes, you need PyYAML to validate and package GLM skills, as the guided packaging and distribution process relies on scripts that parse the structured YAML frontmatter of your SKILL.md files.

Can I use this workflow to benchmark skill performance across multiple iterations?

Yes, you can use this workflow to benchmark skill performance across iterations by generating eval prompts, tracking results, and comparing progress on dashboards to optimize GLM skill reliability.

What are the limitations of manually authoring GLM skills without an evaluation loop?

Manually authoring GLM skills without an evaluation loop increases guesswork and reduces reliability, lacking the structured trigger evaluations and iterative rewrites needed to optimize performance.