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armoin2018
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ailey-admin-tools-optimize

Automate batch normalization and quality scoring of AI-ley kit resources.

4|Updated Jan 2, 2026
Search Tags:#automation#markdown#qa#optimization#cli#yaml#frontmatter
One-click install
npx skills add https://github.com/armoin2018/ai-command-center --skill ailey-admin-tools-optimize
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ailey-admin-tools-optimize
Source: https://github.com/armoin2018/ai-command-center/tree/main/plugins/aicc-admin/skills/ailey-admin-tools-optimize
Command: npx skills add https://github.com/armoin2018/ai-command-center --skill ailey-admin-tools-optimize

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires commander, glob, chalk, and includes scripts (resource) and references (resource) components.

## What problem does it solve? The Admin Optimize Kit tackles the challenge of maintaining consistent structure and quality across AI-ley resources by automatically normalizing frontmatter and footers, extracting code and examples, and applying AI-driven quality scoring at scale. ## Core Features & Use Cases

  • Frontmatter and footer normalization to ensure consistency across instructions, personas, agents, skills, and prompts.
  • Code and example extraction to separate scripts/ and examples/ for maintainability and reuse.
  • AI quality review and scoring with actionable improvement guidance for batch resources.
  • Batch processing of multiple resource types, with optional dry-run and verbose reporting. ### Quick Start Install dependencies and run the optimizer on the desired resource category or all resources.

Frequently Asked Questions about ailey-admin-tools-optimize

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

FAQPage Schema
How do I normalize frontmatter and footers across multiple markdown files in bulk?▼

You can normalize frontmatter and footers across markdown files in bulk by running the optimizer, which applies automated batch processing to instructions, personas, agents, skills, and prompts while preserving the original directory structure.

What is the best way to extract code blocks and examples from markdown documentation?▼

Extracting code and examples from markdown is done via automated CLI processing, which separates embedded scripts into dedicated scripts/ and examples/ directories to improve overall maintainability and component reuse.

Can I run an AI quality review and scoring assessment on existing prompt resources?▼

Yes, you can run AI quality reviews and score assessments on existing prompt resources, generating actionable improvement guidance and exposing detailed results via markdown, HTML, or SUGGESTIONS reporting outputs.

Does the CLI optimizer support a dry-run mode for testing frontmatter normalization?▼

The CLI optimizer supports a dry-run mode for testing frontmatter normalization, allowing you to preview changes and review verbose reporting outputs without permanently modifying your original markdown resource files.

How do I automate QA optimization for large collections of AI agent definitions?▼

You can automate QA optimization for large collections of AI agent definitions by executing the CLI-driven workflow, which applies frontmatter normalization, code extraction, and quality scoring at scale across all specified resource types.

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