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.
npx skills add 0xranx/agentbrief --skill sql-query-buildergit clone --depth 1 https://github.com/0xranx/agentbriefWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/0xranx/agentbrief/sql-query-builder)<a href="https://agentmods.dev/skills/0xranx/agentbrief/sql-query-builder"><img src="https://agentmods.dev/badge/skills/0xranx/agentbrief/sql-query-builder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/0xranx/agentbrief/sql-query-builder"><img src="https://agentmods.dev/badge/skills/0xranx/agentbrief/sql-query-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.00942 |
| Opus 5.5 | $0.00026 | $0.00377 |
| Sonnet 5.5 | $0.00013 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
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 1mo 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.
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
- Use CTEs over subqueries — Readable, debuggable, testable
- Explicit column names — Never
SELECT *in production queries - Consistent formatting — Keywords uppercase, one clause per line
- 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;
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.
- 1mo ago First seen · 116 lines · 64 tokens per session scan A 3a3c4554d42c
sql-query-builder is a skill published in the GitHub repository 0xranx/agentbrief (45 stars, last pushed 6mo ago), licensed MIT. It adds 64 tokens to every session and 942 once invoked, about $0.0003 per session on Opus 5.5. 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.
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