skills
shopify skills plugin for Cursor
cursor.directory·↓ 0
Skill
chompute-bg-remover
>-
# Chompute Background Remover
Removes the background from an image and produces a transparent PNG.
## Prerequisites
This skill requires a Chompute Access Key. Check for the key in this order:
1. `CHOMPUTE_API_KEY`
2. `CLAUDE_PLUGIN_OPTION_chompute_api_key`
3. `CLAUDE_PLUGIN_OPTION_CHOMPUTE_API_KEY`
4. `chompute_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 set `CHOMPUTE_API_KEY`, configure the plugin's Chompute Access Key option,
> or save your key to: `<this skill's directory>/chompute_key.txt`
Stop and do not continue without a valid key file.
If a valid key is present, use it without printing it and tell the user:
> Valid Access Key present. Proceeding...
## Workflow
1. **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:
- a local image file path, or
- a direct HTTP(S) image URL
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 whose background you want removed.
2. **Prepare the API input**:
- If the input is a local file, base64-encode the file contents and send
a `data:<MIME_TYPE>;base64,<BASE64_IMAGE>` value in `image_url`.
- If the input is an HTTP(S) image URL, pass that URL directly in
`image_url` without base64-encoding it yourself.
3. **Call the Chompute API** using the contract below. Use whatever HTTP
method is available in the environment (curl, python, node, etc.).
4. **Parse the response.** On success, extract the output image base64
string and decode it to bytes.
- If the input was a local file, save the output as `<input_stem>_output.png`
in the same directory as the input image file.
- If the input was an image URL, derive the file name from the URL path
and save the output in the current working directory as `<url_stem>_output.png`.
If the URL does not contain a usable file name, use `chompute_output.png`.
5. **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.
## API Contract
### Request
```
POST https://chompute-services.dragonfruit.ai/openai/v1/responses
Content-Type: application/json
Authorization: Bearer <API_KEY>
```
Body:
```json
{
"model": "apple_vision_bg_removal",
"input": [
{
"role": "user",
"content": [
{
"type": "input_image",
"image_url": "<IMAGE_SOURCE>"
}
]
}
]
}
```
Where `<IMAGE_SOURCE>` is either:
- `data:<MIME_TYPE>;base64,<BASE64_IMAGE>` for a local file, or
- a direct HTTP(S) image URL
### Response
The response JSON contains an `output` array. Find the object where
`type` is `"image_generation_call"` — its `result` field holds the
base64-encoded PNG output.
Example (truncated):
```json
{
"output": [
{
"type": "image_generation_call",
"result": "iVBORw0KGgo..."
}
]
}
```
Decode the `result` value from base64 to get the PNG bytes.Skill
chompute-lpr
>-
# Chompute License Plate Recognition
Detects license plates in an image and returns structured JSON with detection
and OCR results.
## Prerequisites
This skill requires a Chompute Access Key. Check for the key in this order:
1. `CHOMPUTE_API_KEY`
2. `CLAUDE_PLUGIN_OPTION_chompute_api_key`
3. `CLAUDE_PLUGIN_OPTION_CHOMPUTE_API_KEY`
4. `chompute_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 set `CHOMPUTE_API_KEY`, configure the plugin's Chompute Access Key option,
> or save your key to: `<this skill's directory>/chompute_key.txt`
Stop and do not continue without a valid key file.
If a valid key is present, use it without printing it and tell the user:
> Valid Access Key present. Proceeding...
## Workflow
1. **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:
- a local image file path, or
- a direct HTTP(S) image URL
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.
2. **Prepare the API input**:
- If the input is a local file, base64-encode the file contents and send
a `data:<MIME_TYPE>;base64,<BASE64_IMAGE>` value in `image_url`.
- If the input is an HTTP(S) image URL, pass that URL directly in
`image_url` without base64-encoding it yourself.
3. **Call the Chompute API** using the contract below. Use whatever HTTP
method is available in the environment (curl, python, node, etc.).
4. **Parse the response.** On success, read the JSON in `output_text`
and extract the LPR results.
- `results` is a list of detections
- each result can include:
- `detection.bbox`
- `detection.confidence`
- `detection.label`
- `ocr.text`
- `ocr.confidence`
- `ocr.char_confidences`
- `lpr_model_config` contains model metadata
5. **Present the result clearly** to the user:
- Do NOT say things like "the chompute-lpr skill found..." or otherwise
make the summary sound like a tool log
- Start with a natural summary in plain English
- If one plate is found, say: `I found 1 license plate in the image. The
plate reads <TEXT>.`
- If multiple plates are found, say how many were detected and list the
detected plate texts that have non-empty OCR values
- If OCR text is missing for a detection, mention that a plate was detected
but the text could not be read confidently
- If `results` is empty, say: `I did not detect any license plate in this
image.`
- After the short summary, add a short non-technical explanation section
called `What this means:`
- In `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>`
- Round confidence values to a friendly percentage format before showing
them to the user
- Keep the explanation concise and user-friendly; assume the reader is not
technical
- Then add a short label: `Technical details (JSON):`
- Only after that label, provide the structured JSON result in a code block
- Do NOT put the raw JSON immediately after the first sentence with no
explanation
6. **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.
## API Contract
### Request
```
POST https://chompute-services.dragonfruit.ai/openai/v1/responses
Content-Type: application/json
Authorization: Bearer <API_KEY>
```
Body:
```json
{
"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, or
- a direct HTTP(S) image URL
### Response
The response JSON contains `output_text`, which is a JSON string representing
the LPR result.
Example (truncated):
```json
{
"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.