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armoin2018
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AI Kit Catalog Manager

Distribute, install, configure, update, and evolve AI kits via a Git-based catalog.

4|Updated Jan 2, 2026
Search Tags:#automation#git#workflow#catalog#version-control#dependency-management#kit-manager
One-click install
npx skills add https://github.com/armoin2018/ai-command-center --skill ai-kit-catalog-manager
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AI Kit Catalog Manager
Source: https://github.com/armoin2018/ai-command-center/tree/main/plugins/aicc-admin/skills/aicc-admin-catalog
Command: npx skills add https://github.com/armoin2018/ai-command-center --skill ai-kit-catalog-manager

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires fs-extra, glob, simple-git, chalk, commander, dotenv, ajv, and includes scripts (resource) components.

What problem does it solve?

Catalogs of AI kits are scattered and risky to manage manually. The AI Kit Catalog Manager provides a Git-based, auditable, and dependency-aware workflow to discover, install, configure, update, remove, and evolve AI kits with reliable targeting and manifest-based tracking.

Core Features & Use Cases

  • Git-based distribution with dependency resolution for seamless kit installation.
  • Surgical removal via manifest tracking for safe cleanup.
  • Pattern-based mapping and agentic filtering to tailor kits to projects.
  • Evolution workflow to contribute changes back to source repositories.
  • Safety rails like dry-run and retry logic to improve reliability.

Quick Start

Install a kit from the catalog and then run configuration and evolution commands to manage it end-to-end.

Frequently Asked Questions about AI Kit Catalog Manager

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

FAQPage Schema
How do I manage AI kit dependencies and version control using a Git catalog?▼

You can manage AI kit dependencies through a Git-based catalog that uses manifest-based tracking for installation and dependency resolution. This approach provides an auditable workflow to distribute, configure, and update kits safely.

What is the safest way to remove installed AI kits from a project?▼

The safest way to remove AI kits is using surgical removal via manifest tracking. This ensures safe cleanup by targeting only the installed files, while self-protection guards prevent accidental catalog-manager modifications during operations.

Does the AI kit workflow support dry-run and retry logic for safe installations?▼

Yes, the AI kit workflow supports safety rails like dry-run and retry logic to improve reliability. These guards help prevent risky manual errors during kit distribution, installation, and evolution back to source repositories.

Can I validate kit manifests and filter mappings across different agentic contexts?▼

Yes, you can validate kit manifests using enforced JSON schema validation and filter them across multiple mappings and agentic contexts. This pattern-based mapping tailors kit configurations to specific project requirements.

How do I contribute changes from an installed AI kit back to its source repository?▼

You can contribute changes back to source repositories using the evolution workflow. This feature allows installed AI kits to evolve safely, syncing modifications back to their original Git-based source through an auditable process.

What are the limitations when updating multiple AI kit mappings simultaneously?▼

A key limitation is that self-protection guards actively prevent catalog-manager modification during operations. While dry-run and retry logic improve reliability, all simultaneous kit updates must pass JSON schema validation to proceed safely.

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