Recipes

amz is built to compose. Every command streams the same record types through the same renderer, so the output of one is the input of the next. These recipes chain them into the work people actually do with Amazon data. They use jq for JSON wrangling, but plain sed/awk versions are shown where they are simpler.

Track a price over time

Append a timestamped price row to a CSV on every run, then watch the file:

amz price B084DWG2VQ -o jsonl \
  | jq -r '[now|todate, .asin, .price, .currency] | @csv' \
  >> price_log.csv

Drop that line in a cron job and you have a price history with no database. To watch a basket, loop a file of ASINs:

while read asin; do amz price "$asin" -o jsonl; done < watchlist.txt \
  | jq -r '[now|todate, .asin, .price] | @csv' >> basket_log.csv

Find the cheapest offer for an ASIN

The Buy Box is not always the cheapest. List every offer and sort:

amz offers B084DWG2VQ -o jsonl \
  | jq -s 'sort_by(.price) | .[0] | {price, condition, seller_name, is_buybox}'

Or only new, Prime-fulfilled options:

amz offers B084DWG2VQ --condition new --prime -o jsonl | jq -s 'min_by(.price)'

Enrich a chart into full product records

Charts give you ASINs and a thumbnail of data. Fan each one out into a full product record:

amz bestsellers electronics -n 25 -o url \
  | sed 's#.*/dp/##; s#/.*##' \
  | xargs -I{} amz product {} -o jsonl > top25.jsonl

Now ask questions of the file. Average discount among the top 25:

jq -s 'map(.savings_pct // 0) | add / length' top25.jsonl

The brands that appear most:

jq -r '.brand' top25.jsonl | sort | uniq -c | sort -rn

Mine the reviews of a product

Pull the corpus and skim sentiment without reading a word:

echo "1-star: $(amz reviews B084DWG2VQ --stars 1 -o jsonl | wc -l)"
echo "5-star: $(amz reviews B084DWG2VQ --stars 5 -o jsonl | wc -l)"

The most-helpful complaints, title and vote count:

amz reviews B084DWG2VQ --stars 1 --sort helpful -n 20 -o jsonl \
  | jq -r '"\(.helpful_votes)\t\(.title)"'

Reviews that mention a keyword:

amz reviews B084DWG2VQ -o jsonl | jq -r 'select(.text | test("battery"; "i")) | .title'

Compare two products side by side

for a in B084DWG2VQ B09B8V1LZ3; do amz product "$a" -o jsonl; done \
  | jq -r '[.asin, .price, .rating, .ratings_count, .rank] | @tsv' \
  | column -t

Scan a search for the best-rated value

Search with refinements, then pick the highest-rated card under a price:

amz search "mechanical keyboard" --min-rating 4 --prime -n 100 -o jsonl \
  | jq -s 'map(select(.price < 120)) | sort_by(-.rating) | .[0:5]'

Walk a brand's catalog

Turn a brand's featured ASINs into full records:

amz brand anker --featured -o url \
  | sed 's#.*/dp/##; s#/.*##' \
  | xargs -I{} amz product {} -o jsonl > anker.jsonl

Build a dataset with the local store

For anything beyond a one-shot, let the queue and DuckDB carry the work. Seed a category's bestsellers, drain the queue, then query with SQL. See crawling at scale for the full treatment.

amz bestsellers electronics -n 100 -o url \
  | sed 's#.*/dp/##; s#/.*##' \
  | amz seed --file -
amz crawl
amz db query "select data->>'brand' brand, count(*) n,
                     round(avg((data->>'price')::double), 2) avg_price
              from products group by brand order by n desc limit 20"

Cross-marketplace price gap

The same ASIN, priced in two storefronts:

for m in us uk de; do
  amz price B084DWG2VQ -m "$m" -o jsonl
done | jq -r '[.marketplace // "?", .price, .currency] | @tsv'

Dry-run before a big crawl

See exactly which URLs a run would hit, across marketplaces, without fetching:

amz product B084DWG2VQ -m jp --dry-run
amz bestsellers electronics -m de --dry-run

Keep iterating for free

Every successful fetch is cached, so once you have pulled a page you can refine the shape of the output as much as you like without touching the network:

amz product B084DWG2VQ -o json                       # first run hits the network
amz product B084DWG2VQ --fields asin,price,ranks     # served from cache
amz product B084DWG2VQ --template '{{.title}} is #{{.rank}}'