Detect and read license plates using Chompute's API, returning structured JSON with bounding boxes, OCR text, confidence, and per-character confidence values. Use when the user asks to detect license plates, read a plate from an image, run ALPR/LPR on an image, or extract plate text and bounding boxes from a local image file or image URL.
How this skill is triggered — by the user, by Claude, or both
Slash command
/license-plate-recognizer:chompute-lprThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Detects license plates in an image and returns structured JSON with detection
Detects license plates in an image and returns structured JSON with detection and OCR results.
This skill requires a Chompute Access Key. Check for the key in this order:
${user_config.chompute_api_key}CHOMPUTE_API_KEYCLAUDE_PLUGIN_OPTION_chompute_api_keyCLAUDE_PLUGIN_OPTION_CHOMPUTE_API_KEYchompute_key.txt in this skill's directory (same folder as this SKILL.md)If none of these exists or the value is empty, tell the user:
You need a Chompute Access Key. Sign up and get one at: https://chompute.ai/skills
Then configure the plugin's Chompute Access Key option, set
CHOMPUTE_API_KEY, or save your key to:<this skill's directory>/chompute_key.txt
Stop and do not continue without a valid Access Key.
If a valid key is present, use it without printing it and tell the user:
Valid Access Key present. Proceeding...
Read the input image attached or mentioned in the message. Do NOT automatically pick images from the folder. The image must be mentioned or attached by the user. The input may be either:
If there is no image attached, mentioned, or linked, tell the user the following and then stop:
Please attach, mention, or provide an image URL for license plate recognition.
Prepare the API input:
data:<MIME_TYPE>;base64,<BASE64_IMAGE> value in image_url.image_url without base64-encoding it yourself.Call the Chompute API using the contract below. Use whatever HTTP method is available in the environment (curl, python, node, etc.).
When constructing the request, use the first non-empty Access Key from the prerequisite list above.
If you write shell commands, prefer this safe export pattern:
PLUGIN_CHOMPUTE_ACCESS_KEY="${user_config.chompute_api_key}"
if [ -n "$PLUGIN_CHOMPUTE_ACCESS_KEY" ] && [ "$PLUGIN_CHOMPUTE_ACCESS_KEY" != '${user_config.chompute_api_key}' ]; then
export CHOMPUTE_API_KEY="$PLUGIN_CHOMPUTE_ACCESS_KEY"
else
export CHOMPUTE_API_KEY=${CHOMPUTE_API_KEY:-${CLAUDE_PLUGIN_OPTION_chompute_api_key:-${CLAUDE_PLUGIN_OPTION_CHOMPUTE_API_KEY:-$(tr -d '[:space:]' < "${CLAUDE_SKILL_DIR}/chompute_key.txt" 2>/dev/null)}}}
fi
Parse the response. On success, read the JSON in output_text
and extract the LPR results.
results is a list of detectionsdetection.bboxdetection.confidencedetection.labelocr.textocr.confidenceocr.char_confidenceslpr_model_config contains model metadataPresent the result clearly to the user:
I found 1 license plate in the image. The plate reads <TEXT>.results is empty, say: I did not detect any license plate in this image.What this means:What this means:, include 2-4 short bullets, depending on what is
available:
Plate text: <TEXT>OCR confidence: <PERCENT>%Detection confidence: <PERCENT>%Location in image: <plain English location if it can be inferred from the bbox, such as center-right or lower-left>Technical details (JSON):On error, show the error details to the user. If the API returns 401, tell the user their Access Key may be invalid or expired and direct them to https://chompute.ai to check their account.
POST https://chompute-services.dragonfruit.ai/openai/v1/responses
Content-Type: application/json
Authorization: Bearer <API_KEY>
Body:
{
"model": "license-plate-recognition",
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "Use the license plate recognition skill on this image."
},
{
"type": "input_image",
"image_url": "<IMAGE_SOURCE>"
}
]
}
]
}
Where <IMAGE_SOURCE> is either:
data:<MIME_TYPE>;base64,<BASE64_IMAGE> for a local file, orThe response JSON contains output_text, which is a JSON string representing
the LPR result.
Example (truncated):
{
"output_text": "{\n \"lpr_model_config\": {\"detector_size\": 384},\n \"results\": [{\"detection\": {\"bbox\": [787, 383, 863, 440], \"confidence\": 0.30, \"label\": \"license_plate\"}, \"ocr\": {\"text\": \"AXWV973\", \"confidence\": 0.99, \"char_confidences\": [0.99, 0.99]}}]\n}"
}
Parse output_text as JSON before presenting the final result.
Provides CDSS development patterns for drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), and alert classification integrated into EMR workflows.
npx claudepluginhub dragonfruit-ai/skills --plugin license-plate-recognizer