Loading...
Loading...
Found 95 Skills
Expertise in maintaining persistent bot memory, synchronizing with previous sessions via the Task Ledger, and preserving decision logs.
Finds and inspects data assets within Google Cloud. Relevant when any of the following conditions are true: 1. The user request involves finding, exploring, or inspecting data assets in Google Cloud, such as: - BigQuery datasets, tables, or views - BigLake catalog or tables - Spanner instances, databases or tables - etc. 2. You need to retrieve the schema, metadata, or governance policies for a GCP data asset. 3. You have a keyword or topic (e.g., "sales data") but lack the specific table or resource ID. 4. You are attempting to find data using `bq ls`, as this skill offers a superior approach. Don't use when: - Assets are outside Google Cloud
Guidelines for identifying and resolving missing Google Cloud authentication and Application Default Credentials (ADC). Use this skill if `gcloud`, `bq`, `dataform`, or Python libraries return authentication errors.
Use this skill to review code. It supports both local changes (staged or working tree) and remote Pull Requests (by ID or URL). It focuses on correctness, maintainability, and adherence to project standards.
Provides expert guidance for troubleshooting Cloud Composer (Apache Airflow) and Orchestration pipelines. Use this skill when the user asks to generate Root Cause Analysis (RCA), troubleshoot or fix a failed pipeline, DAG in Composer environment and generate RCA report.
Use this skill when asked to create a pull request (PR). It ensures all PRs follow the repository's established templates and standards.
Expert guidance for creating modern, intuitive, and visually stunning user interfaces. Use this skill when designing or implementing frontend UIs, components, layout structures, or styling.
Ensures proper Python dependency management, avoiding global `pip install` and adhering to project-specific tooling. Use this skill if any of the following are true: 1. Attempting to run `pip install {package_name}`. 2. Python packages or dependencies need to be added or modified. 3. Initiating a new Python project. 4. Creating a new notebook, even if just using BigQuery cells. 5. Generating Python code that includes `import` statements for third-party libraries. 6. Before executing Python scripts via the terminal to ensure the correct virtual environment is active.
Use these skills when you need to discover and manage PostgreSQL extensions or fine-tune engine-level settings such as memory allocation and server configuration parameters.
Automated data quality and transformation capabilities for Dataform/dbt/BigQuery pipelines. Processes data sourced from BigQuery or Cloud Storage (GCS), applying best practices for data ingestion, movement, schema mapping, and comprehensive data cleaning.
Use these skills when you need to optimize storage, identify index issues, analyze table statistics, or manage autovacuum and tablespace configurations to maintain peak database health.
Use these skills when you need to troubleshoot slow performance, analyze query execution plans, identify resource-heavy processes, and monitor system-level PromQL metrics.