sql-query-builder

A guide for writing SQL, the language used to retrieve and analyze data stored in databases.

In plain words
What is it for?
Use it to write, organize, explain, and improve queries for reports and data analysis.
Why use it?
It helps turn questions such as user counts, funnels, cohorts, and retention into readable queries without unclear or unnecessarily expensive SQL.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/0xranx/agentbrief/sql-query-builder
Any agent
npx skills add 0xranx/agentbrief --skill sql-query-builder
Clone the repo
git clone --depth 1 https://github.com/0xranx/agentbrief

Made for: Claude Code, Codex.

Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 942 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00064 $0.00942
Opus 5 $0.00032 $0.00471
Sonnet 5 $0.00013 $0.00188
Haiku 4.5 $0.00006 $0.00094

Measured 2d ago against content hash 3a3c4554d42c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sql-query-builder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

briefs/data-analyst/skills/sql-query-builder/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SQL Query Builder

You write SQL that is correct, readable, and performant. You optimize for the human reading the query, not just the database executing it.

Style Rules

  1. Use CTEs over subqueries — Readable, debuggable, testable
  2. Explicit column names — Never SELECT * in production queries
  3. Consistent formatting — Keywords uppercase, one clause per line
  4. Comment the "why" — Not what the code does, but why this approach
-- Good: CTEs with clear names
WITH active_users AS (
    SELECT user_id, COUNT(*) AS session_count
    FROM sessions
    WHERE created_at >= CURRENT_DATE - INTERVAL '30 days'
    GROUP BY user_id
    HAVING COUNT(*) >= 3
),
user_revenue AS (
    SELECT user_id, SUM(amount) AS total_revenue
    FROM payments
    WHERE status = 'completed'
    GROUP BY user_id
)
SELECT
    au.user_id,
    au.session_count,
    COALESCE(ur.total_revenue, 0) AS total_revenue
FROM active_users au
LEFT JOIN user_revenue ur ON au.user_id = ur.user_id
ORDER BY ur.total_revenue DESC NULLS LAST;

Common Analysis Patterns

Funnel Analysis

WITH funnel AS (
    SELECT
        COUNT(DISTINCT CASE WHEN step = 'visit' THEN user_id END) AS visitors,
        COUNT(DISTINCT CASE WHEN step = 'signup' THEN user_id END) AS signups,
        COUNT(DISTINCT CASE WHEN step = 'activate' THEN user_id END) AS activated,
        COUNT(DISTINCT CASE WHEN step = 'purchase' THEN user_id END) AS purchasers
    FROM events
    WHERE created_at >= CURRENT_DATE - INTERVAL '30 days'
)
SELECT
    visitors,
    signups,
    ROUND(100.0 * signups / NULLIF(visitors, 0), 1) AS visit_to_signup_pct,
    activated,
    ROUND(100.0 * activated / NULLIF(signups, 0), 1) AS signup_to_activation_pct,
    purchasers,
    ROUND(100.0 * purchasers / NULLIF(activated, 0), 1) AS activation_to_purchase_pct
FROM funnel;

Cohort Retention

WITH user_cohorts AS (
    SELECT
        user_id,
        DATE_TRUNC('week', created_at) AS cohort_week
    FROM users
),
activity AS (
    SELECT
        user_id,
        DATE_TRUNC('week', event_at) AS activity_week
    FROM events
)
SELECT
    uc.cohort_week,
    COUNT(DISTINCT uc.user_id) AS cohort_size,
    COUNT(DISTINCT CASE
        WHEN a.activity_week = uc.cohort_week + INTERVAL '1 week'
        THEN a.user_id
    END) AS week_1_retained,
    ROUND(100.0 * COUNT(DISTINCT CASE
        WHEN a.activity_week = uc.cohort_week + INTERVAL '1 week'
        THEN a.user_id
    END) / NULLIF(COUNT(DISTINCT uc.user_id), 0), 1) AS week_1_retention_pct
FROM user_cohorts uc
LEFT JOIN activity a ON uc.user_id = a.user_id
GROUP BY uc.cohort_week
ORDER BY uc.cohort_week;

Read the full file on GitHub · 116 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 116 lines · 64 tokens per session scan A 3a3c4554d42c

Subscribe to this mod's changes

sql-query-builder is a skill published in the GitHub repository 0xranx/agentbrief (45 stars, last pushed 5mo ago), licensed MIT. It adds 64 tokens to every session and 942 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.