home / skills / openclaw / skills / global-price-comparison
This skill performs global product price comparisons across countries and sources, normalizes offers to USD, and ranks best options for quick decision making.
npx playbooks add skill openclaw/skills --skill global-price-comparisonReview the files below or copy the command above to add this skill to your agents.
---
name: global-price-comparison
description: Discover and compare the same product across multiple countries and source types (official stores, marketplaces, retailers) using Brave and/or Tavily web search, then normalize all offers to USD for ranking and spread analysis. Use when users ask for global product price comparison, cross-country official price checks, or cheapest-market analysis.
---
# Global Price Comparison
Use this skill to run a **global same-product price scan** and output a **USD-normalized comparison**.
## Quick start
```bash
# 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
1. Define exact product variant (model/storage/spec).
2. Run `discover` to get candidate URLs by market and source type.
3. Add verified offers to CSV (`product,country,currency,source_type,source_name,price,url`).
4. Run `compare` to normalize all prices to USD and rank best/worst.
5. Share markdown/JSON/CSV output.
## Commands
### Template
```bash
python scripts/global_price_compare.py template --out /tmp/offers.csv
```
### Discover candidate links
```bash
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:
- `discover` supports `--engine brave|tavily|all` (default `all`).
- 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
```bash
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_store`
- `marketplace`
- `electronics_retailer`
- `general_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`
This skill runs a global same-product price scan and returns a USD-normalized comparison across countries and source types. It discovers candidate listings via Brave and/or Tavily search, accepts verified offers, and produces ranked outputs with spread and delta analysis. Use it to identify the cheapest markets, official price checks, and cross-country price disparities.
The tool can discover candidate product URLs by country and source type using Brave first, with Tavily as a fallback. You populate or verify offers in a simple CSV (product, country, currency, source_type, source_name, price, url). The compare command normalizes each offer to USD, ranks offers, computes deltas and spread (USD and percent), and exports markdown, CSV, or JSON.
What search engines does discovery use?
Discovery uses Brave first and falls back to Tavily when Brave is unavailable or returns no results; you can choose brave, tavily, or all.
How are prices normalized?
Prices are converted to USD using current exchange rates at compare time so offers can be ranked and spread calculated.
What should I include in the CSV?
Include product, country, currency (ISO code), source_type, source_name, price, and URL for each offer; timestamps and condition (new/refurbished) are recommended.