minerals-data

name	minerals-data
description	Query and analyze structured CSV datasets on critical minerals production, trade, and supply chains
metadata	{"openclaw":{"emoji":"📊","requires":{"bins":"[Truncated]"}}}

Minerals Data — Structured CSV Querying

Query and analyze structured CSV datasets from the critical minerals corpus. Supports listing available datasets, describing schemas, filtering, grouping, and aggregation via pandas.

Usage

List available datasets:

python3 {baseDir}/scripts/query_data.py --list

Describe a dataset:

python3 {baseDir}/scripts/query_data.py --dataset usgs/production.csv --describe

Query with DSL:

python3 {baseDir}/scripts/query_data.py --dataset usgs/production.csv --query "groupby:commodity|agg:value:sum|sort:value:desc|head:10"

Filter with pandas expression:

python3 {baseDir}/scripts/query_data.py --dataset usgs/production.csv --filter "year >= 2022"

Combine filter and query:

python3 {baseDir}/scripts/query_data.py --dataset usgs/trade.csv --filter "commodity == 'lithium'" --query "groupby:country|agg:value:sum|sort:value:desc|head:5"

Parameters

Parameter	Description	Default
--list	List all available CSV datasets	-
--dataset	Path to CSV file (relative to corpus dir)	-
--describe	Show schema, dtypes, sample rows, statistics	-
--query	Pipe-delimited DSL for pandas operations	-
--filter	Pandas query expression for filtering	-
--corpus-dir	Directory containing data files	~/critical-minerals-data/
--format	Output format: table, json, csv	table

Query DSL

Pipe-delimited operations that map to pandas:

Operation	Syntax	Example
Group by	groupby:col	groupby:commodity
Aggregate	agg:col:func	agg:value:sum
Sort	sort:col:dir	sort:value:desc
Head	head:n	head:10
Select columns	select:col1,col2	select:commodity,value

Functions: sum, mean, count, min, max, median, std

Examples

# Top producing countries for lithium
python3 {baseDir}/scripts/query_data.py --dataset usgs/production.csv \
  --filter "commodity == 'lithium'" \
  --query "groupby:country|agg:value:sum|sort:value:desc|head:10"

# Year-over-year trade data
python3 {baseDir}/scripts/query_data.py --dataset comtrade/exports.csv \
  --query "groupby:year|agg:value:sum|sort:year:asc"

# Dataset overview
python3 {baseDir}/scripts/query_data.py --dataset worldbank/indicators.csv --describe

Notes

• Requires pandas>=2.0.0 (already in ScienceClaw requirements)
• CSV catalog is cached at ~/critical-minerals-data/.csv_catalog.json
• Handles encoding fallbacks: UTF-8, Latin-1, CP1252
• Filter expressions are sanitized to prevent code injection