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Make and Model Recognition

v1.0.1

Detect the largest vehicle from an image using TrafficEye car-box detection, run make and model recognition for that vehicle, and return all license plates a...

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by@eyedea-ai·duplicate of @radekzc/license-plate-reader
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
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Benign
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Benign
high confidence
✓
Purpose & Capability
Name/description (make & model + plates from an image) match the code and SKILL.md. The only required runtime items are a Python interpreter and a TrafficEye API key, which are proportionate for a client that uploads images to an external recognition API.
✓
Instruction Scope
Runtime instructions and the script focus on validating a local image path, reading that image, uploading it to the configured TrafficEye endpoint, and parsing the JSON response to pick the largest boxed road user. The instructions do not request access to unrelated files, credentials, or external endpoints beyond the API host (api URL is configurable).
✓
Install Mechanism
No install spec; this is essentially an instruction+script skill that runs with the system Python and standard library. Nothing is downloaded or written to disk by an installer.
✓
Credentials
Only TRAFFICEYE_API_KEY is required; other TRAFFICEYE_* variables are optional overrides (API URL, auth mode, field names, timeout). No unrelated credentials or system config paths are requested.
✓
Persistence & Privilege
Skill is not always-enabled, does not modify other skills or global agent configuration, and does not request elevated or persistent system privileges.
Assessment
This skill will upload any image you give it to the TrafficEye service and requires your TRAFFICEYE_API_KEY. Before installing, confirm you trust trafficeye.ai for processing and storage of images (including license plates), check their data-retention/privacy policy, and avoid supplying sensitive images unless you are comfortable with that external processing. Keep your API key secret (do not paste it into chat). You can test behavior offline using the provided sample_response.json to validate selection logic without contacting the API. Autonomous invocation is permitted by default (normal for skills), so only enable the skill for agents you trust to run it.

Like a lobster shell, security has layers — review code before you run it.

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License

MIT-0
Free to use, modify, and redistribute. No attribution required.
Termshttps://spdx.org/licenses/MIT-0.html

Runtime requirements

OSLinux · macOS · Windows
Any binpython3, python
EnvTRAFFICEYE_API_KEY
Primary envTRAFFICEYE_API_KEY

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