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Okf Bigquery

skill-xsavikx-okf-skills-okf-bigquery · by xSAVIKx

Google Cloud BigQuery connector that produces and ingests Open Knowledge Format (OKF) bundles from dataset schemas, table/field descriptions, and metadata. Use when documenting or cataloging BigQuery datasets, extracting schema metadata into OKF, or syncing descriptions back to BigQuery tables and fields.

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Install

$ agentstack add skill-xsavikx-okf-skills-okf-bigquery

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

BigQuery OKF Connector

This skill provides a Go-based CLI tool to convert Google Cloud BigQuery dataset schemas and table/field descriptions into Open Knowledge Format (OKF) bundles, and synchronize comments/descriptions back to BigQuery tables and fields.

When to Use

Use this skill when you need to:

  1. Extract table structures, schemas, field descriptions, and dataset descriptions from a Google Cloud BigQuery dataset into a portable OKF bundle.
  2. Synchronize business descriptions or documentation changes written in an OKF bundle back into native BigQuery table and schema field descriptions.

Setup

The connector requires Go 1.24+ and utilizes the official Google Cloud BigQuery Client:

# Install the published binary (Go 1.24+) — no clone needed:
go install github.com/xSAVIKx/okf-skills/skills/okf-bigquery@v0.1.0

# …or build from a clone of the repository:
cd skills/okf-bigquery
go build -o okf-bigquery .

How to Use

Ensure you have set your GCP credentials. Usually, this is done by setting the GOOGLE_APPLICATION_CREDENTIALS environment variable:

export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials.json"

1. Produce an OKF Bundle

Extract dataset schema metadata and descriptions:

./okf-bigquery produce --project  --dataset  --out  [--tables ] [--sample ] [--profile] [--relationships] [--stats]

2. Ingest / Synchronize an OKF Bundle

Synchronize table and field descriptions from the OKF bundle back to BigQuery:

./okf-bigquery ingest --project  --dataset  --bundle  [--sync]

Parameters:

  • --project (required): Google Cloud Project ID.
  • --dataset (required): Target BigQuery dataset ID.
  • --bundle (required for ingest): Path to the OKF bundle.
  • --out (required for produce): Path to output OKF bundle.
  • --tables (optional): Filter to extract only specific tables.
  • --sample (optional): Embed up to N sample rows per table as a ## Sample section in each table doc (default 0 = none).
  • --profile (optional): Compute per-column statistics (non-null, null, distinct, min, max) and embed a ## Data Profile section.
  • --relationships (optional): Extract informational foreign-key constraints into a ## Relationships section.
  • --stats (optional): Add a ## Stats section with the table row count (from table metadata) and, when a timestamp-like column is detected, a freshness window (min/max). Constraints (## Constraints) and view definitions (## View Definition, views only) are emitted by default. BigQuery has no secondary indexes, so no Indexes section is produced.
  • --sync (optional for ingest): If provided, calls the BigQuery API to update table and schema descriptions.

3. Inspect the Schema (self-description)

Print a machine-readable JSON description of this skill's commands and flags (used by okf-mcp to expose the skill as an MCP tool):

./okf-bigquery schema

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.