SQL Query Explainer Skill

This skill explains SQL queries in plain language, identifies optimisation opportunities, and helps communicate data logic to non-technical stakeholders. It also writes and documents new queries from natural language descriptions.

Required Inputs

• The SQL (Explain/Optimise/Document modes) — the actual query, ideally with the dialect named (Postgres, BigQuery, Snowflake, MySQL…); dialect changes both semantics and the optimisation advice.
• The intent in plain words (Write mode) — what question the data should answer, plus table/column names if known. Without a schema, assumptions get stated, never silently invented.
• Optional but transformative: EXPLAIN/EXPLAIN ANALYZE output and rough table sizes — turns generic advice into advice about your query plan.

Modes

Detect which mode the user needs based on their request:

1. Explain — Translate existing SQL into plain English
2. Optimise — Review SQL for performance issues and suggest improvements
3. Write — Generate SQL from a natural language description
4. Document — Produce a data dictionary or query documentation

Mode 1: Explain

When given a SQL query, produce:

Plain English Summary

[1–3 sentences. What does this query do? What data does it return? Write as if explaining to a business analyst, not a developer.]

Step-by-Step Walkthrough

Break the query into logical sections. For each section:

• Quote the SQL clause
• Explain what it does in plain English
• Flag any complexity (e.g. window functions, subqueries, CTEs)

What the Result Looks Like

[Describe the shape of the output: "Returns one row per user, with columns for X, Y, Z. Ordered by [field] descending."]

Potential Issues to Flag

• [Gotchas, edge cases, or implicit assumptions in this query]
• [e.g. "This will include NULLs in the user_id column if the LEFT JOIN finds no match"]

Mode 2: Optimise

When asked to optimise a query, produce:

Performance Assessment

Rate overall: 🟢 Well-optimised / 🟡 Some improvements possible / 🔴 Significant issues

Issues Found

For each issue:

Issue [N]: [Short name, e.g. "Missing index on join column"]

• What it is: [Plain explanation]
• Why it matters: [Performance impact — e.g. "Full table scan on a 10M row table"]
• Fix:

-- Before
[original snippet]

-- After
[improved snippet]

• Expected improvement: [Estimate if possible]

Optimisation Checklist

• SELECT * used? (Replace with specific columns)
• Implicit type conversions on JOIN/WHERE columns?
• Missing indexes on JOIN or WHERE columns?
• N+1 patterns (queries inside loops)?
• DISTINCT used where GROUP BY would be faster?
• Window functions used where a subquery would be clearer/faster?
• CTEs re-used or materialised unnecessarily?
• Large IN() lists that could use a JOIN instead?

Mode 3: Write

When given a natural language description, generate the SQL query and then explain it using Mode 1.

Ask the user to confirm:

• Database/dialect (PostgreSQL / MySQL / BigQuery / Snowflake / SQLite / Standard SQL)
• Table and column names (if known; otherwise use descriptive placeholder names like users, orders, user_id)
• Any filters, sorting, or aggregation requirements

Produce:

1. The SQL query with inline comments
2. Plain English explanation (Mode 1 format)

Mode 4: Document

When asked to create documentation for a query or table:

Query Documentation

Query: [Name]
Purpose: [One sentence — what business question this answers]
Author: [If provided]
Last reviewed: [If provided]

Inputs:
  - Table: [table_name] — [what it contains]
  - Filter: [any WHERE conditions and their business meaning]

Output columns:
  | Column | Type | Description |
  |--------|------|-------------|
  | [name] | [type] | [plain English description] |

Assumptions:
  - [Any implicit assumptions the query makes]

Known limitations:
  - [Edge cases not handled, data quality dependencies, etc.]

Output Format

Every mode returns the same disciplined shape:

1. The one-line summary — what this query does, in business language ("monthly revenue per region, excluding refunds"), before any SQL talk.
2. The walkthrough or the artifact — mode-dependent: annotated clause-by-clause explanation (Explain), the rewritten query with a diff of what changed and why (Optimise), the new query with stated assumptions (Write), or the doc block (Document).
3. The gotchas — NULL behaviour, join fan-out, timezone traps, and index implications that apply to this query, not generic advice.
4. Verification — a small SELECT the user can run to confirm the query does what the summary claims (row counts before/after, a spot-check predicate).

Quality Checks

• Plain English explanation avoids SQL jargon
• Optimisation suggestions include before/after SQL
• Written queries include inline comments
• Output shape is described (columns, row grain, ordering)
• Dialect-specific syntax is flagged when non-standard

Anti-Patterns

• Restating the SQL in pseudo-code instead of explaining what it does and returns
• Optimisation advice with no before/after query, or no reason the new one is faster
• Ignoring the dialect (writing Postgres-only syntax for a MySQL user)
• "Looks fine" with no read on correctness, performance, or row grain
• Rewriting the query from scratch instead of explaining/optimising the user's

Example Trigger Phrases

• "Explain this SQL query: [paste query]"
• "Optimise this slow query: [paste query]"
• "Write a SQL query that [natural language description]"
• "Document this query for my non-technical stakeholders"
• "Why is this query returning unexpected results?"