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Found 764 Skills
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Use these skills when you need to manage database users, inspect permissions and roles, and verify global configuration parameters related to security and access control.
Skill for BigQuery AI and Machine Learning queries using standard SQL and `AI.*` functions (preferred over dedicated tools).
This skill should be used when the user asks to 'optimize performance', 'check for memory leaks', 'improve performance', 'performance tuning', 'adjust performance', or mentions performance issues in Adobe Animate or CreateJS projects.
CRITICAL RULE: You MUST use this skill whenever the task involves any machine learning tasks or data analysis. Use this skill if the user's prompt or requirements mention any of the following: * Clustering * Classification * Regression * Time series forecasting * Statistical testing * Model comparison * ML * Data analysis SQL/BigQuery ML HANDOFF: If the user requires a SQL solution, use this skill to dictate the ANALYSIS STEPS (e.g., markdown analysis cells, visualization logic), but defer to `bigquery` for all SQL syntax.
This skill helps the agent generate or update orchestration pipeline definitions for Google Cloud Composer to initialize orchestration pipeline or update the orchestration definition for orchestration of various data pipelines, like dbt pipelines, notebooks, Spark jobs, Dataform, Python scripts or inline BigQuery SQL queries. This skill also helps deploy and trigger orchestration pipelines.
Use these skills when you need to provision new Cloud SQL instances, create databases and users, clone existing environments, and monitor the progress of long-running operations.
Integrate with Home Assistant REST and WebSocket APIs. Use when making API calls, managing entity states, calling services, subscribing to events, or setting up authentication. Activates on keywords REST API, WebSocket, API endpoint, service call, access token, Bearer token, subscribe_events.
Discovers and inspects BigQuery Data Transfer Service (DTS) configurations. Use this to identify existing ingestion pipelines and extract datasource or transfer config metadata for data pipelines. Use when a user asks for ingestion scenarios while building or managing data pipelines or when a user asks to "ingest" or "add" data that may already be managed by a DTS transfer.
Use these skills when you need to handle advanced data intelligence and predictive tasks. Use when a user asks "why" data changed or needs future projections. Provides automated insight generation and time-series forecasting.
This skill should be used when the user asks to "create an esphome config", "set up an esp32 device", "configure esphome yaml", "add a sensor to esphome", "fix esphome compile error", or mentions "gpio pin assignment", "wifi setup", "ota update", or error messages like "Unknown platform", "GPIO already in use", "Could not compile", or "WiFi connection failed". Provides rapid ESPHome configuration generation, troubleshooting, and validation.
Assist with Kubernetes interactions including debugging (kubectl logs, describe, exec, port-forward), resource management (deployments, services, configmaps, secrets), and cluster operations (scaling, rollouts, node management). Use when working with kubectl, pods, deployments, services, or troubleshooting Kubernetes issues.