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Found 711 Skills
Develop custom Home Assistant integrations, config flows, entities, and platforms. Use when working with manifest.json, custom components, config_flow.py, entity base classes, or device registry. Activates on keywords: integration, custom component, config flow, entity, platform, manifest.json, device_info.
Configure Home Assistant Lovelace dashboards, cards, views, and themes. Use when working with dashboard YAML, card configuration, view layouts, custom cards, or frontend theming.
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.
Use these skills when you need to troubleshoot slow performance, analyze query execution plans, identify resource-heavy processes, and monitor system-level PromQL metrics.
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 monitor replication health, manage sync states between nodes, and ensure the high availability and data distribution of your AlloyDB cluster.
Provides guidance for writing, packaging and executing Apache Beam pipelines on GCP using Cloud Dataflow. Use when: - Creating an Apache Beam Dataflow pipeline. - Creating a Google Flex Template.
Use these skills when you need to handle large-scale data exploration and dataset management. Use when users need to find data assets or run SQL at scale. Provides metadata discovery and query execution across the data warehouse.
Interact with GitLab repositories, merge requests, and APIs using the GITLAB_TOKEN environment variable. Use when working with code hosted on GitLab or managing GitLab resources.
**STOP AND VERIFY**: Before running any command or tool that results in irreversible data loss, you MUST obtain explicit user consent. When in doubt, ask. It is better to wait for confirmation than to accidentally delete production data or critical project assets. Use this for: - SQL: DROP TABLE/VIEW/SCHEMA/DATABASE, TRUNCATE, or broad DELETE (missing WHERE or using 1=1). - Cloud Storage: gsutil rm or gcloud storage rm targeting production data or critical buckets. - Infrastructure: gcloud projects delete, deleting Spanner/BigQuery/Dataproc resources, deleting secrets, or KMS key destruction.
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.
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.