Overview
The Amazon Repricing Diagnosis Assistant is used to answer "how should prices be adjusted to remain stable," organize materials, clean data, and identify action items. It is suitable for operations, product, sales, and software to use after clarifying business goals, content materials, or analysis targets. It combines inputs such as products, stores or operational materials, pasted product information, store pages, review materials, competitor information, sales targets, or channel requirements, organizes key context, and outputs product/store diagnoses, selling point and risk alerts, and operational action recommendations for further execution, review, or delivery. Capability positioning supplement: the skills.sh hot signal shows that amazon-repricing-strategy has user demand; the platform displays it as an ecommerce_retail business assistant directory that outputs reviewable diagnoses, recommendations, or production preparation packages.
When to Use
Applicable Scenarios
- Users raise questions such as "how should prices be adjusted to remain stable" and need to quickly break down goals, determine priorities, and form executable results
- Operations, product, sales, and software need to generate product/store diagnoses, selling point and risk alerts, and operational action recommendations around the Amazon Repricing Diagnosis Assistant
- Users have already prepared operational goals (explaining the click, conversion, repurchase, inventory, profit, review, or campaign goals they hope to optimize.), target buyers (explaining the target buyers, market, platform, channel, or usage scenario.), and product or store links (filling in publicly accessible product, store, competitor, review, or platform page links.), and hope to organize them into executable analysis or optimization results
- Users need to turn materials related to the Amazon Repricing Diagnosis Assistant into clear conclusions, priorities, and next-step actions
How to Invoke
Call the AI Skills API directly through the exported Python runner:
Command Examples
Basic Call
python3 scripts/run.py --params '{}'
Call with Common Parameters
python3 scripts/run.py --params '{"goal":"运营目标"}'
Parameter Description
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
goal |
string | No | - | Explain the click, conversion, repurchase, inventory, profit, review, or campaign goals you hope to optimize |
audience |
string | No | - | Explain the target buyers, market, platform, channel, or usage scenario |
materialUrl |
string | No | - | Fill in publicly accessible product, store, competitor, review, or platform page links; a complete accessible URL must be provided |
materialFile |
string | No | - | Upload product materials, review sheets, competitor screenshots, operational data, store plans, or material files |
materialText |
string | No | - | Paste product information, store pages, review materials, competitor information, sales targets, or channel requirements |
brandRequirements |
string | No | - | Supplement platform rules, price boundaries, inventory constraints, brand restrictions, content that cannot be promised, or key points for manual review |
See the complete machine-readable parameter structure at references/form-schema.json。
Parameter Value Reference
This skill currently has no categorical parameters that require additional lookup.
Supported Input Formats
The current skill directly accepts JSON parameters; if the parameters contain a link field, please pass a complete, accessible URL.
Example request
The following example parameters can be passed directly to scripts/run.py, and the runner will send them to the AI Skills API.
python3 scripts/run.py --params '{"goal":"运营目标"}'
Equivalent --params JSON：
{
"goal": "运营目标"
}
Example return result
{
"success": true,
"data": {
"message": "示例结果请以技能真实返回结构为准。"
},
"meta": {
"executionTime": 842,
"cached": false
}
}
Deliverables
- Product/store diagnosis, selling points and risk warnings, operational action recommendations: organize core results around the user's goals that can be directly read, reviewed, or delivered.
- Interpretation of input materials: combine operational goals (specify the click-through, conversion, repurchase, inventory, profit, review, or campaign goals you want to optimize.), target buyers (specify the target buyers, market, platform, channel, or usage scenario.), and product or store links (provide publicly accessible links to products, stores, competitors, reviews, or platform pages.) to extract key context and the basis for judgment.
- Next steps: provide priorities, execution recommendations, or a content framework that can be further processed.
Suggestions for using the results
- First determine whether the output answers the user's core questions about the "Amazon Price Adjustment Diagnostic Assistant".
- Then check whether the results cover product/store diagnosis, selling points and risk warnings, operational action recommendations, and whether clear next steps are provided.
- If the input materials are insufficient, it is recommended to have the user supplement the goals, audience, constraints, or original materials before running.
Preparation before running
AISKILLS_BASE_URL: defaulthttps://ai-skills.aiAISKILLS_API_KEY: required, used for authentication callsAISKILLS_TENANT_ID: defaultdefault