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AvaTar-ArTs
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workspace-ecosystem-audit

Inventory local AI agent directories and map component roles and dependencies.

Updated May 11, 2026
Search Tags:#automation#audit#assets#inventory#references#ecosystem#summarize
One-click install
npx skills add https://github.com/AvaTar-ArTs/my-supremepowers --skill workspace-ecosystem-audit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: workspace-ecosystem-audit
Source: https://github.com/AvaTar-ArTs/my-supremepowers/tree/main/qwen_skills/workspace-ecosystem-audit
Command: npx skills add https://github.com/AvaTar-ArTs/my-supremepowers --skill workspace-ecosystem-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps teams audit and document local AI ecosystems by scanning agent directories, aggregating assets, references, and scripts, and producing an actionable inventory with risk signals.

Core Features & Use Cases

  • Automated inventory of agent components across multiple directories and roots.
  • Unified summaries of roles, dependencies, and provenance for audits and onboarding.
  • Risk-aware insights by flagging missing references, stale assets, and potential inconsistencies.

Quick Start

Run the inventory workflow to generate a comprehensive ecosystem manifest for your local AI agents.

Frequently Asked Questions about workspace-ecosystem-audit

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

FAQPage Schema
How do I inventory and map local AI agent components across multiple directories?▼

To inventory and map local AI agent components, you can scan multiple agent directories like .claude, .gemini, and .qwen to aggregate scripts, assets, and references into a comprehensive ecosystem manifest.

What is the best way to audit an AI ecosystem for missing references and stale assets?▼

Auditing an AI ecosystem for missing references and stale assets involves scanning agent directories to produce risk-aware summaries that flag potential inconsistencies and track provenance.

Does this automated inventory workflow work with multiple agent frameworks like .claude and .gemini?▼

Yes, this automated inventory workflow works with multiple agent frameworks like .claude and .gemini by scanning multiple roots to collect component dependencies and roles.

How do I generate a unified summary of agent roles and dependencies for onboarding?▼

Generating a unified summary of agent roles and dependencies requires running an inventory workflow that aggregates local AI ecosystem assets into actionable risk-aware documentation.

What are the limitations of auditing local AI ecosystems with deterministic data collection?▼

The limitations of auditing local AI ecosystems with deterministic data collection include relying on existing agent directory structures and only flagging risks based on found scripts, references, and assets.

Why do I need to track provenance across scripts and assets in an AI ecosystem?▼

Tracking provenance across scripts and assets in an AI ecosystem is needed to ensure structured summaries accurately reflect component roles, reveal dependencies, and support reliable audits.

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