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 agentmods add commands/justvinhhere/bigquery-expert/bq-design-tablegit 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/commands/justvinhhere/bigquery-expert/bq-design-table)<a href="https://agentmods.dev/commands/justvinhhere/bigquery-expert/bq-design-table"><img src="https://agentmods.dev/badge/commands/justvinhhere/bigquery-expert/bq-design-table.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 | $0.00025 | $0.00371 |
| Opus 5 | $0.00013 | $0.00186 |
| Sonnet 5 | $0.00005 | $0.00074 |
| Haiku 4.5 | $0.00003 | $0.00037 |
Grade A, and why
bq-design-table 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 6d 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.
What it actually says
BigQuery Table Design
Design an optimal BigQuery table schema using the bigquery-schema-design skill.
Instructions
-
Determine the input:
- If an argument is provided, treat it as the data description or sample data.
- If a file path is provided, read that file for sample data or an existing schema.
- If no argument is provided, ask the user to describe their data, its volume, and primary query patterns.
-
Gather context by asking (if not already provided):
- What queries will run against this table? (filter columns, aggregations, joins)
- Approximate data volume and growth rate (rows/day, total size).
- Write pattern: batch loads, streaming inserts, or frequent updates?
- Data retention requirements (how long to keep data).
-
Design the schema using the
bigquery-schema-designskill:- Choose partitioning strategy based on primary filter column and data volume.
- Choose clustering columns based on secondary filters and join keys.
- Decide nested vs. flat based on entity relationships and update patterns.
- Select optimal data types for each column.
- Consider table type (native, materialized view, external) based on access patterns.
-
Output the recommendation in the standard schema design format:
- Design decisions summary (partitioning, clustering, nesting, data types).
- Complete
CREATE TABLEDDL with all options. - Rationale explaining trade-offs and why this design fits the workload.
- If applicable, suggest materialized views or complementary tables.
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.
- 6d ago First seen · 35 lines · 25 tokens per session scan A 1f2381fb1919
bq-design-table is a command published in the GitHub repository justvinhhere/bigquery-expert (15 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 371 once invoked, about $0.0001 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.
Other commands, from other repositories
setup-bigquery.es
Command "setup-bigquery.es" from minicoohei/ai-agent-camp, covering configuración de autenticación bigquery / gcp, step 0: verificar el progreso de configuración, lo que hará en esta sesión, verificación de preparación and step 1: instalación de gcloud cli.
setup-bigquery
Command "setup-bigquery" from minicoohei/ai-agent-camp, covering bigquery / gcp 認証セットアップ, step 0: セットアップ進捗の確認, このセッションでやること, 準備チェック and step 1: gcloud cli のインストール.
init
Initialize configurations for Supabase local development.
database-setup
Use when a project needs to store data and has no database yet. Setting up Supabase, creating tables, writing queries, and connecting them to the frontend. Written for designers.
ingest
Manually add knowledge to the Weaviate store.
json.batch_get
Read multiple JSON values by document and path.