Global Price Comparison
Use this skill to run a global same-product price scan and output a USD-normalized comparison.
Quick start
# 1) Create a starter CSV template
python scripts/global_price_compare.py template --out /tmp/offers.csv
# 2) (Optional) discover candidate links by country/source type
# Uses Brave and Tavily when available (BRAVE_API_KEY / TAVILY_API_KEY)
python scripts/global_price_compare.py discover \
--product "iPhone 16 Pro 256GB" \
--countries US,JP,DE,UK \
--source-types official_store,marketplace,electronics_retailer \
--engine all \
--out /tmp/discover.json
# 3) Fill /tmp/offers.csv with verified offers, then compare
python scripts/global_price_compare.py compare \
--input /tmp/offers.csv \
--format markdown
Workflow
- Define exact product variant (model/storage/spec).
- Run
discoverto get candidate URLs by market and source type. - Add verified offers to CSV (
product,country,currency,source_type,source_name,price,url). - Run
compareto normalize all prices to USD and rank best/worst. - Share markdown/JSON/CSV output.
Commands
Template
python scripts/global_price_compare.py template --out /tmp/offers.csv
Discover candidate links
python scripts/global_price_compare.py discover \
--product "PlayStation 5 Slim" \
--countries US,JP,DE \
--source-types official_store,marketplace,electronics_retailer \
--engine all \
--count 3 \
--format markdown
Notes:
discoversupports--engine brave|tavily|all(defaultall).- For
all, it uses Brave first and only falls back to Tavily when Brave fails or returns no results. - Set one or both keys as needed:
BRAVE_API_KEY,TAVILY_API_KEY. - Source types are intentionally generic (not local-store specific).
Compare offers in USD
python scripts/global_price_compare.py compare \
--input /tmp/offers.csv \
--format markdown \
--out /tmp/comparison.csv
Outputs include:
- ranked offers by USD price
- delta vs best offer
- spread in USD and %
Defaults
Default country set:
US, UK, JP, DE, FR, CA, AU, SG, HK, TW
Default source types:
official_storemarketplaceelectronics_retailergeneral_retailer
Data/quality guardrails
Before finalizing recommendations:
- confirm same model/variant
- confirm tax/shipping basis consistency
- separate new vs refurbished/used
- keep URL + timestamp evidence
Detailed schema + guardrails:
references/data-shape-and-source-types.md
Resources
- Script:
scripts/global_price_compare.py - Reference:
references/data-shape-and-source-types.md