skills.rest
adrianolaselva
adrianolaselvaCOMMUNITY
Explore all skills by this author →
Website𝕏@adrianolaselvaGitHub

dataql-analysis

Run SQL queries on CSV, JSON, Parquet, and Excel files with DataQL.

4|Updated Jan 16, 2026
Search Tags:#cli#json#sql#csv#analyze#parquet#dataql
One-click install
npx skills add https://github.com/adrianolaselva/dataql --skill dataql-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dataql-analysis
Source: https://github.com/adrianolaselva/dataql/tree/main/cmd/skillsctl/embedded/skills/dataql-analysis
Command: npx skills add https://github.com/adrianolaselva/dataql --skill dataql-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data analysts often need to run SQL queries on file datasets (CSV, JSON, Parquet, Excel) but lack a lightweight, repeatable tool. This Skill provides a focused CLI approach to analyze data files with DataQL, delivering quick, reproducible insights.

Core Features & Use Cases

  • Run SQL queries against CSV, JSON, Parquet, Excel and other supported formats using DataQL.
  • Join datasets from multiple files, filter, aggregate, and export results to CSV or JSONL.
  • Quick schema inspection and straightforward data transformation for pipelines.

Quick Start

Use the DataQL CLI to run a simple query against a local file, for example: dataql run -f data.csv -q "SELECT * FROM data LIMIT 5"

Frequently Asked Questions about dataql-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run SQL queries on CSV and Parquet files without a database?▼

You can run SQL queries directly on CSV and Parquet files using DataQL to filter, aggregate, and join datasets. This provides a lightweight, repeatable CLI approach for generating actionable insights without needing a traditional database setup.

Can I join data from multiple JSON and Excel files into a single output?▼

Yes, you can join datasets from multiple files including JSON, CSV, Parquet, and Excel formats. The Skill supports joining, filtering, and aggregating data across these sources, then exporting the combined results to CSV or JSONL formats.

What is the best way to inspect the schema of a local data file before analysis?▼

The best way to inspect a file schema is using the DataQL CLI to run a query like SELECT * FROM data LIMIT 5 against your local file. This allows quick schema inspection and straightforward data transformation for downstream pipelines.

Does this CLI data analysis approach require any external dependencies or database servers?▼

No external dependencies or database servers are required. The Skill operates as a focused CLI tool with zero dependencies, enabling you to analyze file datasets with deterministic commands directly in your local environment.

How do I export SQL query results from DataQL for downstream pipelines?▼

You can export SQL query results from DataQL to either CSV or JSONL formats. This structured output capability makes the results suitable for direct integration into downstream data pipelines and automated workflows.

Why use a CLI tool for SQL data analysis instead of a traditional database engine?▼

A CLI tool provides quick, reproducible insights for file datasets without the overhead of database setup. It satisfies functional requirements of running deterministic commands, inferring schemas, and producing structured outputs suitable for pipelines.

Related Skills

Explore More Data & Analytics Skills→
adrianolaselvaadrianolaselva
4

dataql-quick

Query CSV and JSON data files with DataQL via Bash.

Community
Advanced
dotlab-hqdotlab-hq

query-writing

Generate and execute SQL SELECT queries with schema inspection and validation.

Official
Advanced
darblexdarblex

ai-automation-workflows

Automate complex AI workflows via CLI and Python SDK.

Community
Advanced
PovertyActionPovertyAction
3

duckdb

Run SQL queries and transform CSV, Parquet, JSON, and Excel files via the DuckDB CLI.

Official
Intermediate