More screenshots
Works with the AI tools you already use
🗂️ Model Inventory Auditor
The Model Inventory Auditor scans your entire codebase to generate an "AI Bill of Materials" (AIBOM).
Secure checkout via Stripe
See it in action
You say
Evaluate my repository and give me an AI model bill of materials.
Your agent does
AI Model Inventory
- OpenAI: gpt-4o, gpt-3.5-turbo (Hardcoded in
LLMService.ts) - Anthropic: claude-3-5-sonnet-20240620 (Env:
CLAUDE_MODEL_ID)
Risk Findings
- [MIA001] Hardcoded 'gpt-4o' literal found in
services/openai.py - [MIA005] OpenAI pinned in
package.jsonwith no fallback.
What you get
About this skill
What it does
The Model Inventory Auditor scans your entire codebase to generate an "AI Bill of Materials" (AIBOM). It identifies every LLM provider, model ID, and API configuration used across your scripts, dependency manifests, and environment files. It goes beyond a simple search by mapping logical models to specific providers and detecting architectural risks.
Why use this skill
Manually tracking every model call in a sprawling application is error-prone. This skill automates the audit process to protect you from vendor lock-in and production failures. It flags hardcoded model IDs, inconsistent naming conventions, and dangerous single-provider dependencies that could represent a single point of failure for your AI features.
Supported tools
- Languages: Python, JavaScript, TypeScript
- Config: JSON, YAML, .env files
- Package Managers: pip (requirements.txt, pyproject.toml), npm/yarn (package.json)
- Frameworks: Compatible with any LLM integration (LangChain, OpenAI SDK, Anthropic SDK, etc.)
The Output
You receive a structured inventory of all detected AI dependencies followed by a set of targeted findings (MIA codes). These findings highlight specific risks like pinned versions without fallbacks or model IDs missing environment-based indirection.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
Fresh listing
Recently published to Agensi
30-day refund
Not a fit? Get your money back
Trust & safety
Security scanned
Verified clean 1 month ago
- 30-day refund guarantee
- One-time purchase, yours forever
- Secure checkout via Stripe
Creator
JustHandled Labs builds focused agent skills for the work nobody wants to do by hand. Each one is a single repeatable job done well: catching the security and data mistakes that quietly ship, keeping docs and tests honest, gating the commands an agent is about to run, sharpening writing, and handling the founder chores around launches, outreach, and brand setup. Not generic AI productivity. Specific workflows that are easy to run, review, and repeat. Maintained by H.J. Westerfield, with a background in communications, editing, project coordination, customer support, and practical AI systems. Tools for people who want useful automation without theatrical complexity.
Frequently Asked Questions
Popular in AI Agents & LLM Ops
designing-hybrid-context-layers
Architects the right retrieval strategy for every query — teaching your agent when to use RAG, a knowledge graph, or a temporal index instead of defaulting to vector search for everything.
prompt-engineer
Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.
benchmarking-ai-agents-beyond-models
Published AI benchmarks measure brains in jars. They test models in isolation or within a single reference harness — and then attribute all performance to the model. This skill teaches you to decompose agent performance into its two actual components: model capability and harness multiplier. The result is evaluations that predict real-world behavior instead of benchmark theater.
codex-grade-coding
Turn your AI agent into a senior engineer with strict task classification and verification-driven coding protocols.