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Found 644 Skills
Create and debug Home Assistant automations, scripts, blueprints, and Jinja2 templates. Use when working with triggers, conditions, actions, automation YAML, scripts, blueprints, or template expressions. Activates on keywords: automation, trigger, condition, action, blueprint, script, template, jinja2.
Set up Home Assistant energy monitoring with dashboards, solar, grid, and device tracking. Use when configuring energy sensors, utility meters, statistics, or analyzing consumption. Activates on keywords: energy dashboard, solar, grid, consumption, kWh, utility meter, power monitoring, state_class, device_class: energy.
SOC II triage workflow for creating Linear tickets, branches, OpenSpec proposals, commits, and PRs. Use when asked to triage an issue, create a triage ticket, or start SOC II workflow.
Scaffold and automate Grafana plugin projects using @grafana/create-plugin. Use when creating panel plugins, data source plugins, app plugins, or backend plugins. Handles project scaffolding, Docker dev environment setup, and plugin configuration.
Configure Home Assistant Assist voice control with pipelines, intents, wake words, and speech processing. Use when setting up voice control, creating custom intents, configuring TTS/STT, or building voice satellites. Activates on keywords: Assist, voice control, wake word, intent, sentence, TTS, STT, Piper, Whisper, Wyoming.
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.
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.
Build modern data apps, dashboards, and interactive reports using either React + Vite or Streamlit. Includes optional Gemini Data Analytics chat integration for an AI powered "chat with your data" experience. Relevant when any of the following conditions are true: 1. User explicitly requests to build a data dashboard, data application, or visualization UI, and the UI pulls data from a GCP database (defaulting to BigQuery unless otherwise specified). 2. You need to generate a frontend web application to interact with, query, and visualize data from GCP data sources. 3. User wants to build a "chat with your data" experience or integrate the Gemini Data Analytics chat API into a web interface. Do NOT use when any of the following conditions are true: 1. The request is for building backend-only services. 2. The request is for simple CLI scripts or command-line applications. 3. The web application is not data-centric or does not involve visualizing/querying data from GCP sources.
A repository of BigQuery-specific logic, knowledge, and specialized standards. Use this skill whenever you are doing anything with BigQuery, including: 1. BigQuery query optimization 2. BigFrames Python code 3. BigQuery ML/AI functions.
Use these skills when you need to monitor replication health, manage sync states between nodes, and ensure the high availability and data distribution of your AlloyDB cluster.
Expertise in generating clean, correct, and efficient Dataform pipeline code for BigQuery ELT. Use this when creating or modifying Dataform pipelines, actions, or source declarations, when Dataform, SQLX, or BigQuery are mentioned in a transformation, when data needs to be ingested from GCS into BigQuery via Dataform, or when setting up a new Dataform project or configuring workflow_settings.yaml.
Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.