skill-creator

Author SKILL.md files, run trigger evaluations, and optimize descriptions.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/samurano/ai-brain --skill skill-creator-samurano
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skill-creator
Source: https://github.com/samurano/ai-brain/tree/main/.agents/skills/skill-creator
Command: npx skills add https://github.com/samurano/ai-brain --skill skill-creator-samurano

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

The Skill Creator provides an end-to-end workflow for building AI skills: drafting SKILL.md, defining evaluation prompts, running trigger evaluations, analyzing results, and iterating to improve performance and clarity. It codifies the process so teams can produce reliable, reusable skills that trigger reliably across contexts.

Core Features & Use Cases

  • End-to-end skill lifecycle: draft, test, iterate, and deploy skills with consistent frontmatter and body content.
  • Built-in evaluation and benchmarking: generate eval sets, run triggers against multiple backends, record results, and produce benchmark summaries.
  • Description optimization: automatically refine the triggering description to improve skill invocation accuracy.
  • Regression awareness: track history across iterations and surface insights for when changes help or hurt performance.
  • Packaging and reuse: package skills into portable artifacts and reuse components across projects.

Quick Start

Draft a new skill by outlining its goal and triggers, create its SKILL.md, run an initial evaluation, and iterate based on the results.

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 iterate AI skills with an end-to-end workflow?

To create and iterate AI skills, draft a SKILL.md file defining triggers and goals, run trigger evaluations, analyze benchmark results, and refine the description to improve invocation accuracy.

What is the process for evaluating and benchmarking prompt-engineering skills?

Evaluating and benchmarking prompt-engineering skills involves generating evaluation sets, running triggers against multiple backends, recording results, and producing benchmark summaries to track performance across iterations.

How can I optimize skill triggers to ensure reliable invocation across different contexts?

You can optimize skill triggers by automatically refining the triggering description within the SKILL.md frontmatter, testing it against generated evaluation prompts, and iterating based on benchmark summaries.

Can I package AI skills into portable artifacts and reuse components across projects?

Yes, you can package completed AI skills into portable artifacts and reuse components across different projects, ensuring reliable and consistent skill triggers throughout your team's workflow.

How do I track regression and performance history when refining AI skills?

You track regression and performance history by recording evaluation results across iterations, allowing the system to surface insights on when changes help or hurt the skill's overall clarity and performance.