What You’ll Build
A Python script that pulls running shoe data from Amazon, Walmart, and Nike.com and outputs a ranked, cross-retailer price comparison — all from a single prompt to your AI coding assistant.
The Nimble Agents skill handles the rest:
- Finds pre-built agents for sites already in the gallery (Amazon, Walmart)
- Generates a custom agent for any site that isn’t (Nike.com)
- Writes the full analysis script using the agents it collected
Prerequisites
You’ll also need Python 3.8+ and the Nimble SDK:
pip install nimble_python
Step 1: Give Claude the Prompt
Open a new session in Claude Code (or Cursor) and paste this prompt:
Build a Python script that runs competitive analysis on running shoes across
Amazon, Walmart, and Nike.com. Use Nimble for all data collection.
For each retailer:
- Search the Nimble agent gallery for an existing agent. Use it if one exists.
- If no agent exists for that site, use the Nimble Agents skill to generate
a custom one, then publish it for reuse.
The script should collect product name, price, rating (if available), and URL
from each source, then print a unified list sorted by price and save the full
results to shoe_analysis.json.
My Nimble API key is set as the NIMBLE_API_KEY environment variable.
Build a Python script that runs competitive analysis on running shoes across
Amazon, Walmart, and Nike.com. Use Nimble for all data collection.
For each retailer:
- Search the Nimble agent gallery for an existing agent. Use it if one exists.
- If no agent exists for that site, use the Nimble Agents skill to generate
a custom one, then publish it for reuse.
The script should collect product name, price, rating (if available), and URL
from each source, then print a unified list sorted by price and save the full
results to shoe_analysis.json.
My Nimble API key is set as the NIMBLE_API_KEY environment variable.
What Claude Does Next
Claude works through the task autonomously using the Nimble Agents skill. Here’s what happens under the hood:
Searches the gallery for Amazon
Claude calls nimble_agents_list with the query "amazon" and finds amazon_serp — a pre-built agent for Amazon search results. It inspects the schema and confirms keyword is the required input.
Searches the gallery for Walmart
Same flow for Walmart — finds walmart_search, confirms it takes a keyword param and returns product name, price, and rating.
Finds no agent for Nike — generates one
No public agent exists for nike.com. Claude calls nimble_agents_generate with a description of what’s needed, waits for the agent to be created, runs a test extraction, and publishes it as nike_running_shoes_plp.
Writes the script
With all three agents confirmed, Claude writes shoe_analysis.py using the Nimble Python SDK and the agent names it just collected.
import json
import os
from nimble_python import Nimble
nimble = Nimble(api_key=os.environ["NIMBLE_API_KEY"])
KEYWORD = "running shoes"
NIKE_URL = "https://www.nike.com/w/mens-running-shoes"
def fetch_amazon():
result = nimble.agent.run(
agent="amazon_serp",
params={"keyword": KEYWORD}
)
products = result.data.parsing.get("parsed", [])
return [
{
"source": "Amazon",
"name": p.get("product_name"),
"price": p.get("price"),
"rating": p.get("average_rating"),
"url": p.get("product_url"),
}
for p in products
if p.get("price")
]
def fetch_walmart():
result = nimble.agent.run(
agent="walmart_search",
params={"keyword": KEYWORD}
)
products = result.data.parsing.get("parsed", [])
return [
{
"source": "Walmart",
"name": p.get("product_name"),
"price": p.get("price"),
"rating": p.get("rating"),
"url": p.get("product_url"),
}
for p in products
if p.get("price")
]
def fetch_nike():
result = nimble.agent.run(
agent="nike_running_shoes_plp",
params={"url": NIKE_URL}
)
products = result.data.parsing.get("parsed", [])
return [
{
"source": "Nike",
"name": p.get("product_name"),
"price": p.get("price"),
"colors": p.get("colors", []),
"url": p.get("url"),
}
for p in products
if p.get("price")
]
def run_analysis():
print(f'Competitive analysis: "{KEYWORD}"\n')
all_products = []
print("Fetching Amazon...")
all_products.extend(fetch_amazon())
print("Fetching Walmart...")
all_products.extend(fetch_walmart())
print("Fetching Nike...")
all_products.extend(fetch_nike())
sorted_products = sorted(all_products, key=lambda x: x["price"])
print(f"\n{'=' * 55}")
print(f" RESULTS — {len(all_products)} products across 3 retailers")
print(f"{'=' * 55}\n")
for i, p in enumerate(sorted_products[:15], 1):
stars = f" ★{p['rating']}" if p.get("rating") else ""
colors = f" ({len(p['colors'])} colors)" if p.get("colors") else ""
print(f"{i:2}. [{p['source']:7}] ${p['price']:<8.2f} {p['name']}{stars}{colors}")
with open("shoe_analysis.json", "w") as f:
json.dump(all_products, f, indent=2)
print(f"\nFull results saved to shoe_analysis.json")
if __name__ == "__main__":
run_analysis()
The exact agent names (e.g. nike_running_shoes_plp) are chosen by the skill at generation time. Claude will use whatever names were returned and write the script accordingly — you don’t need to track them manually.
Step 2: Run the Script
Expected output:
Competitive analysis: "running shoes"
Fetching Amazon...
Fetching Walmart...
Fetching Nike...
=======================================================
RESULTS — 62 products across 3 retailers
=======================================================
1. [Walmart ] $19.98 Athletic Works Men's Knit Running Shoe ★4.1
2. [Walmart ] $29.98 Starter Men's Athletic Running Shoe ★4.0
3. [Walmart ] $44.97 Skechers Go Run Consistent ★4.3
4. [Amazon ] $54.99 New Balance Men's 515 V3 Sneaker ★4.5
5. [Walmart ] $54.98 Nike Downshifter 12 ★4.5
6. [Amazon ] $64.99 Adidas Men's Runfalcon 3.0 ★4.6
7. [Amazon ] $69.99 Nike Men's Revolution 7 ★4.6
8. [Nike ] $110.00 Nike Pegasus Trail 5 (3 colors)
9. [Amazon ] $119.95 Brooks Ghost 15 ★4.7
10. [Nike ] $130.00 Nike Pegasus 41 (5 colors)
...
Full results saved to shoe_analysis.json
What’s Next
