SQL Query Generation

What it does

When your agent uses it

• The user needs a new query from a business question
• Use query-optimization when an existing query
• Execution plan is slow

Example prompts

• “/sql-query-generation”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Parse the natural language request. Extract the analytical intent: what metric is being asked for, which entities are involved, what…
2. 2Map to the database schema. Identify the relevant tables and columns from the schema. Resolve ambiguous references (e.g., "sales" could…
3. 3Select the appropriate query constructs. Choose between simple aggregation, window functions, CTEs, or subqueries based on complexity. Use…
4. 4Generate the SQL query. Write syntactically correct SQL with consistent formatting: uppercase keywords, lowercase identifiers, aliased…
5. 5Validate and optimize. Run EXPLAIN (or EXPLAIN ANALYZE) on the generated query to inspect the execution plan. Look for full table scans…
6. 6Return results with explanation. Present the query alongside a plain-language explanation of what it does, the expected output format, and…

What it can do on your machine

Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are sql).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

SQL Query Generation loads about 2.3k tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 736 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~73

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 736 words, ~2,290 tokens.

name

sql-query-generation

description

Generate SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.

license

MIT

metadata.author

awesome-ai-agent-skills

metadata.version

1.0.0

WITH current_period AS (
    SELECT
        c.customer_id,
        c.customer_name,
        SUM(oi.quantity * oi.unit_price) AS total_spent,
        COUNT(DISTINCT o.order_id) AS order_count
    FROM customers c
    JOIN orders o ON c.customer_id = o.customer_id
    JOIN order_items oi ON o.order_id = oi.order_id
    WHERE o.order_date >= CURRENT_DATE - INTERVAL '12 months'
    GROUP BY c.customer_id, c.customer_name
),
previous_period AS (
    SELECT
        c.customer_id,
        SUM(oi.quantity * oi.unit_price) AS prev_total_spent
    FROM customers c
    JOIN orders o ON c.customer_id = o.customer_id
    JOIN order_items oi ON o.order_id = oi.order_id
    WHERE o.order_date >= CURRENT_DATE - INTERVAL '24 months'
      AND o.order_date < CURRENT_DATE - INTERVAL '12 months'
    GROUP BY c.customer_id
),
top_categories AS (
    SELECT DISTINCT ON (c.customer_id)
        c.customer_id,
        pc.category_name AS favorite_category,
        COUNT(*) AS category_orders
    FROM customers c
    JOIN orders o ON c.customer_id = o.customer_id
    JOIN order_items oi ON o.order_id = oi.order_id
    JOIN products p ON oi.product_id = p.product_id
    JOIN product_categories pc ON p.category_id = pc.category_id
    WHERE o.order_date >= CURRENT_DATE - INTERVAL '12 months'
    GROUP BY c.customer_id, pc.category_name
    ORDER BY c.customer_id, COUNT(*) DESC
)
SELECT
    cp.customer_name,
    cp.total_spent,
    cp.order_count,
    tc.favorite_category,
    pp.prev_total_spent,
    ROUND(
        (cp.total_spent - COALESCE(pp.prev_total_spent, 0))
        / NULLIF(pp.prev_total_spent, 0) * 100, 1
    ) AS spending_change_pct
FROM current_period cp
LEFT JOIN previous_period pp ON cp.customer_id = pp.customer_id
LEFT JOIN top_categories tc ON cp.customer_id = tc.customer_id
ORDER BY cp.total_spent DESC
LIMIT 5;

-- Expected output:
-- customer_name | total_spent | order_count | favorite_category | prev_total_spent | spending_change_pct
-- Acme Corp     | 284,500.00  | 47          | Electronics       | 198,200.00       | 43.5
-- GlobalTech    | 231,800.00  | 38          | Software          | 245,100.00       | -5.4
-- ...

SELECT product_name, SUM(quantity * unit_price) AS revenue
FROM order_items oi, products p, orders o
WHERE oi.product_id = p.product_id
  AND oi.order_id = o.order_id
  AND o.order_date BETWEEN '2024-01-01' AND '2024-12-31'
GROUP BY product_name
ORDER BY revenue DESC;

Seq Scan on orders o  (cost=0.00..98456.00 rows=1245000)
  Filter: (order_date >= '2024-01-01' AND order_date <= '2024-12-31')
  Rows Removed by Filter: 3755000
Hash Join  (cost=98456.00..245678.00 rows=3200000)
Sort  (cost=312456.00..312460.00 rows=8500)
  Sort Method: external merge  Disk: 4096kB

-- Step 1: Create index (one-time)
CREATE INDEX idx_orders_date ON orders (order_date)
    INCLUDE (order_id);

-- Step 2: Rewrite with explicit joins and date index hint
SELECT
    p.product_name,
    SUM(oi.quantity * oi.unit_price) AS revenue
FROM orders o
JOIN order_items oi ON o.order_id = oi.order_id
JOIN products p ON oi.product_id = p.product_id
WHERE o.order_date BETWEEN '2024-01-01' AND '2024-12-31'
GROUP BY p.product_name
ORDER BY revenue DESC;

-- After optimization: 0.34 seconds (36x faster)
-- EXPLAIN now shows:
-- Index Scan on idx_orders_date (rows=1245000, actual=1243892)
-- Merge Join (cost reduced by 85%)
-- Sort Method: quicksort  Memory: 512kB

© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in data-and-analytics/sql-query-generation of seb1n/awesome-ai-agent-skills.

Open the folder on GitHubat commit 75865a5