skill-creator

Automate the end-to-end workflow for creating and evaluating GLM Code skills.

Updated Apr 20, 2026
One-click install
npx skills add https://github.com/Kraits/cxc-ace --skill skill-creator-kraits
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/Kraits/cxc-ace/tree/main/skills-backup/skill-creator
Command: npx skills add https://github.com/Kraits/cxc-ace --skill skill-creator-kraits

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

The Skill Creator helps teams design, refine, and benchmark their own GLM Code skills by providing a repeatable framework to capture intent, write entry points (SKILL.md), run evaluations, and iterate based on feedback.

Core Features & Use Cases

  • Automates the end-to-end lifecycle of skill development: intent capture, SKILL.md drafting, eval/test prompts, and result-driven iteration.
  • Generates structured evaluation artifacts (evals.json, grading.json, benchmark.json) and optional reports to guide improvements.
  • Supports rapid iteration: duplicate runs, compare with baselines, and optimize triggering descriptions to increase triggering accuracy.

Quick Start

Draft a new skill, run an initial evaluation, review results, and iterate until your skill meets your triggering and performance goals.

Frequently Asked Questions about skill-creator

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

FAQPage Schema
How do I automate GLM Code skill creation and evaluation?

Automating GLM Code skill creation involves capturing intent, drafting SKILL.md entry points, running iterative evals, and generating structured benchmark reports to guide performance improvements.

What is the best way to refine prompt-engineering descriptions for better triggering?

Refining prompt-engineering descriptions requires running iterative evaluations against baselines, analyzing aggregated benchmark data, and optimizing trigger accuracy through structured feedback loops.

How do I set up the environment to build and benchmark skills?

Setting up to build and benchmark skills requires installing the pyyaml dependency to parse configuration files and generate structured evaluation artifacts like evals.json and benchmark.json.

Can I compare my current skill iteration against a previous baseline?

Comparing skill iterations against baselines is supported by duplicating eval runs and aggregating benchmark results to measure triggering accuracy and performance changes across versions.

What formats are generated when running skill evaluations?

Running skill evaluations generates structured artifacts including evals.json for test prompts, grading.json for scoring, and benchmark.json to inform improvements across iterations.