Loading...
Loading...
Found 29 Skills
Data analysis, SQL queries, BigQuery operations, and data insights. Use for data analysis tasks and queries.
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
Google Cloud Platform (GCP) development best practices for Cloud Functions, Cloud Run, Firestore, BigQuery, and Infrastructure as Code.
Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.
Google Cloud Platform CLI - manage GCP resources including Compute Engine, Cloud Run, GKE, Cloud Functions, Storage, BigQuery, and more.
Google Cloud Platform CLI (gcloud, gcloud storage, bq). Use when: managing GCP resources, deploying to Cloud Run/Cloud Functions/GKE/App Engine, working with Cloud Storage, BigQuery, IAM, Compute Engine, Cloud SQL, Pub/Sub, Secret Manager, Artifact Registry, Cloud Build, Cloud Scheduler, Cloud Tasks, Vertex AI, VPC/networking, DNS, logging/monitoring, or any GCP service. Also covers: authentication, project/config management, CI/CD integration, serverless deployments, container registry, docker push to GCP, managing secrets, Workload Identity Federation, and infrastructure automation.
Systematic 7-step methodology for comprehensive patent prior art searches and patentability assessments using BigQuery and CPC classification
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Google Cloud Platform SDK integration. Cloud Functions, Firestore, Cloud Storage, Pub/Sub, BigQuery, and Cloud Run. Node.js and Python client libraries. USE WHEN: user mentions "GCP", "Google Cloud", "Cloud Functions", "Firestore", "Cloud Storage", "Pub/Sub", "BigQuery", "Cloud Run", "Firebase" DO NOT USE FOR: AWS services - use `aws`; Azure services - use `azure`; Firebase Auth - use auth skills
This skill should be used when the user asks to "query BigQuery with Python", "use the google-cloud-bigquery SDK", "load data into BigQuery", "define a BigQuery schema", or needs guidance on best practices for the Python BigQuery client library.
Creates and maintains dlt (data load tool) pipelines from APIs, databases, and other sources. Use when the user wants to build or debug pipelines; use verified sources (e.g. Salesforce, GitHub, Stripe) or declarative REST API or custom Python; configure destinations (e.g. DuckDB, BigQuery, Snowflake); implement incremental loading; or edit .dlt config and secrets. Use when the user mentions data ingestion, dlt pipeline, dlt init, rest_api_source, incremental load, or pipeline dashboard.
Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.