feat: add 1P BigQuery skill for guided data analysis#4678
feat: add 1P BigQuery skill for guided data analysis#4678caohy1988 wants to merge 1 commit intogoogle:mainfrom
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request introduces a new first-party BigQuery data analysis skill to the ADK framework. This skill provides structured, guided workflows and comprehensive reference materials, allowing agents to leverage both raw BigQuery tools and curated expertise. The primary goal is to standardize and improve the quality of data analysis workflows, thereby reducing the need for extensive manual prompt engineering and making BigQuery interactions more efficient and reliable for agents. Highlights
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Code Review
The pull request introduces a new 1P BigQuery skill for guided data analysis, which is a valuable addition for improving agent workflows. The design document, README, and skill content (SKILL.md, error_handling.md, schema_exploration.md, sql_patterns.md) are well-structured and provide clear guidance. The unit tests cover the skill's functionality and integration effectively. However, there are a couple of areas related to credential handling and string formatting in the sample agent that could be improved for better security and readability.
| credentials_config = BigQueryCredentialsConfig( | ||
| client_id="YOUR_CLIENT_ID", | ||
| client_secret="YOUR_CLIENT_SECRET", |
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Hardcoding client_id and client_secret directly in the agent.py file, even as placeholders, is not ideal for security and maintainability. It's best practice to load these sensitive credentials from environment variables or a secure configuration store to prevent accidental exposure and facilitate easier management across different environments.
| credentials_config = BigQueryCredentialsConfig( | |
| client_id="YOUR_CLIENT_ID", | |
| client_secret="YOUR_CLIENT_SECRET", | |
| credentials_config = BigQueryCredentialsConfig( | |
| client_id=os.environ.get("BIGQUERY_CLIENT_ID", "YOUR_CLIENT_ID"), | |
| client_secret=os.environ.get("BIGQUERY_CLIENT_SECRET", "YOUR_CLIENT_SECRET"), | |
| ) |
| pip install google-adk[bigquery] | ||
| ``` | ||
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| 2. Set up OAuth credentials: |
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It's generally recommended to avoid hardcoding sensitive information like client_id and client_secret directly in code files, even if they are placeholders in a sample. This practice can lead to accidental exposure if not handled carefully. Consider suggesting the use of environment variables or a secure configuration management system for these credentials.
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Pre-packaged BigQuery data analysis skill following the agentskills.io specification. Users combine BigQueryToolset (raw tools) with SkillToolset (curated guidance) for progressive disclosure of workflow instructions and reference materials. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
BigQueryToolset(raw tools) withSkillToolset(curated guidance) for progressive disclosure of workflow instructions and reference materialsNew files (10)
src/google/adk/tools/bigquery/skills/bigquery-data-analysis/SKILL.mdsrc/google/adk/tools/bigquery/skills/bigquery-data-analysis/references/sql_patterns.mdsrc/google/adk/tools/bigquery/skills/bigquery-data-analysis/references/schema_exploration.mdsrc/google/adk/tools/bigquery/skills/bigquery-data-analysis/references/error_handling.mdsrc/google/adk/tools/bigquery/bigquery_skill.pyget_bigquery_skill()convenience loadercontributing/samples/1p_bigquery_skill/__init__.pycontributing/samples/1p_bigquery_skill/agent.pycontributing/samples/1p_bigquery_skill/README.mdcontributing/samples/1p_bigquery_skill/DESIGN.mdtests/unittests/tools/bigquery/test_bigquery_skill.pyTest plan
pytest tests/unittests/tools/bigquery/test_bigquery_skill.py -v— 7/7 tests pass_validate_skill_dirreturns empty problems list)./autoformat.shcleanadk web contributing/samples(requires BQ credentials)🤖 Generated with Claude Code