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ποΈ Model Inventory Auditor
Find pattern-based model/provider references in reviewed source and prepare an explicitly incomplete inventory for deployment review.
$13
ποΈ Model Inventory Auditor
Example session with this skill installed
Run the packaged risky-sample fixture with Model Inventory Auditor v1.1.0. Explain the findings without treating detected strings as complete runtime inventory.
- Read your context and instructions
- Compiled the model inventory auditor
Packaged fixture run, not customer evidence
Command: python scripts/scan_model_inventory.py fixtures/risky-sample
Coverage
incomplete. The run detects OpenAI-associated model strings gpt-4o at app.py:6 and gpt-4o-2024-08-06 at app.py:7. Paths below are package-relative.
All seven reported findings
- MIA001, medium, app.py:6: model-like literal gpt-4o to review.
- MIA004, medium, app.py:6: review configuration indirection.
- MIA001, medium, app.py:7: model-like literal gpt-4o-2024-08-06 to review.
- MIA004, medium, app.py:7: review configuration indirection.
- MIA002, high, summary: only one model provider detected, OpenAI. This does not prove no fallback exists.
- MIA003, medium, summary: logical model gpt-4o has inconsistent IDs.
- MIA005, low, package.json:1: pinned provider dependency with no detected alternative.
Next step
Review deployment identity and runtime use. The optional JSON handoff starts incomplete with unresolved provider/platform identity; do not convert these detections into a complete bill of materials or a retirement verdict.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
What you get
Model Inventory Auditor v1.1.0 provides a local read-only scanner, editable detection patterns, fixtures, tests and an optional JSON retirement handoff. Findings include model strings and file/line references, not copied source lines.
Who it helps
A developer or maintainer starting a model-dependency review. Pattern matches may be comments or examples rather than runtime use. A single detected provider does not prove that the application lacks a fallback.
Before you start
Downloadable skill, not a hosted app. Use Python 3.10+ and a compatible agent or terminal. Standard library only. Extract the complete folder and supply reviewed, non-secret source/configuration. Environment files, including .env.example, are excluded; other secrets are not reliably detected.
First run
python scripts/scan_model_inventory.py fixtures/risky-sample
Review the sample before substituting approved source. Add --json for the incomplete retirement handoff and read the included retirement-handoff reference before resolving platform/provider identities.
Limits
No complete runtime discovery, model calls or source edits. Dynamic routing, unsupported providers and skipped content remain coverage gaps. Missing, linked or unreadable inputs produce CANNOT_ASSESS, not a clean assessment.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
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