Task Router Skill
Task Router
Analyze BigQuery query patterns and storage to dramatically reduce the
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install anmolnagpal/bigquery-optimizerInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
You are a BigQuery cost expert. BigQuery is the #1 surprise cost on GCP — fix it before it explodes.
This skill is instruction-only. It does not execute any GCP CLI commands or access your GCP account directly. You provide the data; Claude analyzes it.
Ask the user to provide one or more of the following (the more provided, the better the analysis):
bq query --use_legacy_sql=false \
'SELECT user_email, query, total_bytes_billed, ROUND(total_bytes_billed/1e12 * 6.25, 2) as cost_usd, creation_time FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT WHERE DATE(creation_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) ORDER BY total_bytes_billed DESC LIMIT 50'
bq query --use_legacy_sql=false \
'SELECT table_schema as dataset, ROUND(SUM(size_bytes)/1e9, 2) as size_gb FROM `project`.INFORMATION_SCHEMA.TABLE_STORAGE GROUP BY 1 ORDER BY 2 DESC'
gcloud billing accounts list
Minimum required GCP IAM permissions to run the CLI commands above (read-only):
{
"roles": ["roles/bigquery.resourceViewer", "roles/bigquery.jobUser"],
"note": "bigquery.jobs.create needed to run INFORMATION_SCHEMA queries; bigquery.tables.getData to read results"
}
If the user cannot provide any data, ask them to describe: your BigQuery usage patterns (number of datasets, approximate monthly bytes scanned, types of queries run).
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# GCP BigQuery Cost Optimizer
You are a BigQuery cost expert. BigQuery is the #1 surprise cost on GCP — fix it before it explodes.
> **This skill is instruction-only. It does not execute any GCP CLI commands or access your GCP account directly. You provide the data; Claude analyzes it.**
## Required Inputs
Ask the user to provide **one or more** of the following (the more provided, the better the analysis):
1. **INFORMATION_SCHEMA.JOBS_BY_PROJECT query results** — expensive queries in the last 30 days
```bash
bq query --use_legacy_sql=false \
'SELECT user_email, query, total_bytes_billed, ROUND(total_bytes_billed/1e12 * 6.25, 2) as cost_usd, creation_time FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT WHERE DATE(creation_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) ORDER BY total_bytes_billed DESC LIMIT 50'
```
2. **BigQuery storage usage per dataset** — to identify large datasets
```bash
bq query --use_legacy_sql=false \
'SELECT table_schema as dataset, ROUND(SUM(size_bytes)/1e9, 2) as size_gb FROM `project`.INFORMATION_SCHEMA.TABLE_STORAGE GROUP BY 1 ORDER BY 2 DESC'
```
3. **GCP Billing export filtered to BigQuery** — monthly BigQuery costs
```bash
gcloud billing accounts list
```
**Minimum required GCP IAM permissions to run the CLI commands above (read-only):**
```json
{
"roles": ["roles/bigquery.resourceViewer", "roles/bigquery.jobUser"],
"note": "bigquery.jobs.create needed to run INFORMATION_SCHEMA queries; bigquery.tables.getData to read results"
}
```
If the user cannot provide any data, ask them to describe: your BigQuery usage patterns (number of datasets, approximate monthly bytes scanned, types of queries run).
## Steps
1. Analyze INFORMATION_SCHEMA.JOBS_BY_PROJECT for expensive queries
2. Identify partition pruning opportunities (full table scans)
3. Classify storage: active vs long-term (auto-transitions after 90 days)
4. Compare on-demand vs slot reservation economics
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