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sql-workflow

Guide schema exploration and verification before writing SQL queries.

475|25|Updated Mar 31, 2026
Search Tags:#workflow#verification#sql#cte#schema-exploration#query-building
One-click install
npx skills add https://github.com/SignalPilot-Labs/SignalPilot --skill sql-workflow-signalpilot-labs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sql-workflow
Source: https://github.com/SignalPilot-Labs/SignalPilot/tree/main/benchmark/signalpilot-plugin/skills/sql-workflow
Command: npx skills add https://github.com/SignalPilot-Labs/SignalPilot --skill sql-workflow-signalpilot-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Before writing SQL queries, data teams often struggle to anticipate output shapes, validate data availability, and plan an incremental, verifiable query strategy. This Skill provides a structured pre-SQL workflow to infer results, explore schemas, and establish verification checks to reduce rework and errors.

Core Features & Use Cases

  • Output shape inference and cardinality clues from questions to guide query design.
  • Efficient schema exploration and iterative CTE-based query building.
  • Structured verification loop (row count, NULL audit, fan-out check, sample inspection) and error recovery protocol.
  • Saving outputs to result.sql and result.csv and turn-budget management for controlled experimentation.
  • Applicable to analysts building complex data pipelines, data engineers prototyping queries, and QA validating results.

Quick Start

Activate this skill before composing SQL queries to guide exploration, build queries step by step, verify intermediate results, and save the final artifacts to result.sql and result.csv.

Frequently Asked Questions about sql-workflow

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

FAQPage Schema
How do I build and verify complex SQL workflows using CTEs across multiple tables?▼

Build complex SQL workflows by guiding schema exploration, estimating cardinality, and constructing iterative CTEs. Verify intermediate results using row counts, NULL audits, fan-out checks, and sample inspections before saving final outputs.

What is the best way to plan SQL queries and check data availability before writing code?▼

Plan SQL queries by inferring output shapes and cardinality clues from your questions first. Explore schemas to validate data availability, then establish a structured verification loop to reduce rework and errors.

How do I validate intermediate SQL results and prevent fan-out issues during query building?▼

Validate intermediate SQL results through a structured verification loop executing row counts, NULL audits, fan-out checks, and sample inspection. This stepwise verification ensures data integrity throughout the iterative CTE building process.

Can I save iterative SQL query outputs to SQL and CSV files automatically?▼

Yes, you can save iterative SQL query outputs automatically. The workflow specifies saving the final verified query artifacts directly to result.sql and result.csv files for downstream usage and controlled experimentation.

Does this SQL workflow approach support turn-budget management and error recovery?▼

Yes, this SQL workflow approach supports turn-budget management and error recovery. It includes a structured protocol for error recovery during query building, ensuring controlled experimentation within allocated processing limits.

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