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transilienceai
transilienceaiOFFICIAL
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discover-ai

Inventory AI/ML services across cloud accounts and GitHub repositories.

145|28|Updated Apr 4, 2026
Search Tags:#aws#github#azure#code-scanning#discover#ai-services#cloud-inventory
One-click install
npx skills add https://github.com/transilienceai/shasta --skill discover-ai-transilienceai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: discover-ai
Source: https://github.com/transilienceai/shasta/tree/main/.claude/skills/discover-ai
Command: npx skills add https://github.com/transilienceai/shasta --skill discover-ai-transilienceai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Identify and inventory AI/ML services deployed across cloud accounts and GitHub repositories, giving you a current view of your AI footprint.

Core Features & Use Cases

  • Cloud service discovery: Detects AI-related services on AWS and Azure to build a consolidated inventory.
  • Codebase AI usage scanning: Scans GitHub repos for embedded AI usage or AI SDKs to surface SDK versions and integration points.
  • Usage & governance scenarios: Useful for AI governance, due-diligence, and cost/risks assessment across multi-cloud environments.

Quick Start

Configure shasta.config.json with your python_cmd, aws_profile, azure_subscription_id, and github_repos, then run the discovery to generate an AI system inventory.

Frequently Asked Questions about discover-ai

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

FAQPage Schema
How do I inventory AI services deployed across my cloud accounts?▼

To inventory AI services across cloud accounts, you must configure shasta.config.json with your aws_profile and azure_subscription_id, then run the discovery process to detect and consolidate your AI service usage.

Can I scan GitHub repositories for AI SDK usage and integration points?▼

Yes, you can scan GitHub repositories for AI SDK usage by specifying your target github_repos in shasta.config.json, allowing the tool to surface embedded AI footprints and SDK versions in your codebase.

What is the best way to assess AI governance risks across multi-cloud environments?▼

Assessing AI governance risks across multi-cloud environments requires analyzing both cloud service inventories and codebase AI usage to identify deployed services, integration points, and associated costs or risks.

Do I need to configure python_cmd to discover AI services on AWS and Azure?▼

Yes, configuring python_cmd in shasta.config.json is required to enable the execution environment needed for discovering AI services across your AWS and Azure cloud accounts.

How does codebase AI usage scanning help with due diligence?▼

Codebase AI usage scanning supports due diligence by detecting embedded AI SDKs and integration points in GitHub repositories, providing a complete view of your AI footprint and associated risks.

What limitations exist when discovering AI services across multiple cloud providers?▼

Discovery is limited to scanning AWS and Azure for AI services and GitHub repositories for SDK footprints, requiring proper configuration of shasta.config.json for each targeted platform.

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