home / skills / openclaw / skills / global-price-comparison

global-price-comparison skill

/skills/wsjwong/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-comparison

Review the files below or copy the command above to add this skill to your agents.

Files (4)
SKILL.md
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---
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`

Overview

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.

How this skill works

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.

When to use it

  • Compare the same model across multiple countries to find cheapest market pricing
  • Verify official store listed prices versus marketplaces and retailers
  • Gather evidence and timestamps for cross-country price audits
  • Estimate import cost advantages after currency normalization
  • Create purchasability reports for procurement or resale decisions

Best practices

  • Define the exact product variant (model, storage, spec) before discovery to avoid mismatches
  • Use the discover step to capture candidate URLs, then manually verify offers in the CSV
  • Ensure consistency on tax and shipping basis (tax-inclusive vs tax-exclusive) before comparing
  • Separate new, refurbished, and used offers to avoid invalid comparisons
  • Keep source URL and timestamp evidence for every offer and record the currency code explicitly

Example use cases

  • Global consumer comparison: find cheapest country to buy a phone model including official stores and marketplaces
  • Retail arbitrage: identify price spreads large enough to justify import and resale after fees
  • Procurement check: confirm official regional MSRP differences for budgeting
  • Market research: quantify price dispersion across regions for a given electronics SKU
  • Compliance or audit: produce timestamped evidence of listed prices per country and source type

FAQ

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.