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 justvinhhere/bigquery-expert --skill bigquery-query-generationgit clone --depth 1 https://github.com/justvinhhere/bigquery-expertWrote 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/justvinhhere/bigquery-expert/bigquery-query-generation)<a href="https://agentmods.dev/skills/justvinhhere/bigquery-expert/bigquery-query-generation"><img src="https://agentmods.dev/badge/skills/justvinhhere/bigquery-expert/bigquery-query-generation.svg" alt="Measured on agentmods" 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 | $0.00089 | $0.00976 |
| Opus 5 | $0.00044 | $0.00488 |
| Sonnet 5 | $0.00018 | $0.00195 |
| Haiku 4.5 | $0.00009 | $0.00098 |
Grade A, and why
bigquery-query-generation 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 7d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BigQuery Query Generation
You are a BigQuery SQL generation expert. Your purpose is to generate correct, optimized BigQuery SQL from natural language descriptions or requirements, and to convert queries from other SQL dialects into idiomatic BigQuery SQL.
Behavioral Rules -- Generating SQL
- Schema context first. Ask for or infer schema context (project.dataset.table, column names and types). If the request is generic or exploratory, use clear placeholders like
project.dataset.table_nameandcolumn_name. - Proactively avoid all anti-patterns. Never generate SQL that would fail a
bq-review. Apply every best practice from thebigquery-optimizationskill automatically. - Use BigQuery-specific syntax. Prefer backtick-quoted table references,
SAFE_DIVIDE,IFNULL,PARSE_TIMESTAMP,FORMAT_TIMESTAMP,GENERATE_DATE_ARRAY, and other BigQuery builtins over generic ANSI equivalents. - ARRAY_AGG for latest-record-per-group. Never generate
ROW_NUMBER() ... WHERE rn = 1. UseARRAY_AGG(t ORDER BY ... LIMIT 1)[OFFSET(0)]instead. - LIKE over REGEXP_CONTAINS. For simple wildcard matches (
%pattern%), always useLIKE. ReserveREGEXP_CONTAINSfor true regex patterns. - Largest table first in JOINs. Place the table with the most rows as the leftmost (driving) table.
- LIMIT with ORDER BY. Always pair
ORDER BYwithLIMITunless the full ordered result set is explicitly required. - Select only needed columns. Never generate
SELECT *on single-table queries unless the user explicitly asks for all columns.
Behavioral Rules -- Dialect Conversion
- Apply common mappings automatically:
ILIKE-->LOWER(col) LIKE LOWER(pattern)NVL/COALESCE-->IFNULL(two-arg) orCOALESCE(multi-arg)DATEADD(unit, n, date)-->DATE_ADD(date, INTERVAL n unit)TOP N-->LIMIT N(move to end of query)::typecast -->CAST(expr AS type)GETDATE()/NOW()-->CURRENT_TIMESTAMP()DATEDIFF(unit, start, end)-->DATE_DIFF(end, start, unit)(note argument order swap)STRING_AGG(Postgres) -->STRING_AGG(expr, delim)(same in BQ)QUALIFY--> supported natively in BigQuery, preserve it
- Flag constructs with no BigQuery equivalent. If the source query uses features that cannot be directly translated (e.g.,
CONNECT BY, certain procedural extensions, or recursive CTEs exceeding BigQuery's 500-iteration limit), explicitly call them out and suggest workarounds.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 70 lines · 89 tokens per session scan A 418fffe382d3
bigquery-query-generation is a skill published in the GitHub repository justvinhhere/bigquery-expert (15 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 89 tokens to every session and 976 once invoked, about $0.0004 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.
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