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Found 51 Skills
Primary entry point for building, managing, and orchestrating data pipelines on Google Cloud. Guides users to the appropriate skill for dbt, Dataflow (Apache Beam), Dataform, Spark (Dataproc Serverless), BigQuery Data Transfer Service (DTS) or orchestration pipeline using Cloud Composer. Clarify requirements and resolve ambiguity for creating, updating and running data pipelines.
Use this skill when building data pipelines, ETL/ELT workflows, or data transformation layers. Triggers on Airflow DAG design, dbt model creation, Spark job optimization, streaming vs batch architecture decisions, data ingestion, data quality checks, pipeline orchestration, incremental loads, CDC (change data capture), schema evolution, and data warehouse modeling. Acts as a senior data engineer advisor for building reliable, scalable data infrastructure.
Create efficient data pipelines with tf.data
Use this skill for data pipeline work — ingestion with dlt, transformations with sqlmesh, analytics with DuckDB/MotherDuck, DataFrames with polars, notebooks with marimo, and project management with uv.
Focused Signals scout for PostHog projects moving data through pipelines. Watches the three delivery surfaces — CDP destinations and transformations (hog functions), batch exports, and hog flows (workflows/messaging) — for contradictions between configured state and actual delivery: functions the watcher quietly degraded or disabled, failure rates stepping above a pipeline's own baseline, batch export runs failing or stalling (a growing data gap), and active flows failing for the people they trigger on. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
Build and troubleshoot Cloudflare Basin analytics workflows with Basin Pipelines, Basin Catalog, and Basin SQL. Use for streaming data into R2 Iceberg tables, managing catalogs, or querying those tables; also use for requests using the former Data Platform, Pipelines, R2 Data Catalog, or R2 SQL names.
Use this skill when the user asks about Goldsky Mirror pipelines — creating, deploying, operating, or troubleshooting Mirror. Triggers on: 'Mirror pipeline', 'goldsky pipeline apply', 'sync subgraph to database', 'mirror vs turbo', 'direct indexing', 'mirror pipeline YAML', 'mirror pipeline pause/stop/restart'. Also use this skill when the user wants to sync a Goldsky subgraph into a database or message queue — Mirror is the only pipeline product that supports subgraph sources. For new pipelines that don't need a subgraph source, the turbo-builder skill is usually a better fit. Do NOT trigger on 'goldsky turbo' commands or generic 'build a pipeline' requests without subgraph context — those belong to the turbo skills.
Stream Light Protocol account state via Laserstream gRPC. Covers token accounts, mint accounts, and compressible PDAs with hot/cold lifecycle tracking. Use when building custom data pipelines, aggregators, or indexers.
Develop Microsoft Fabric Spark/data engineering workflows with intelligent routing to specialized resources. Provides core workspace/lakehouse management and routes to: data engineering patterns, development workflow, or infrastructure orchestration. Use when the user wants to: (1) manage Fabric workspaces and resources, (2) develop notebooks and PySpark applications, (3) design data pipelines and orchestration, (4) provision infrastructure as code. Triggers: "develop notebook", "data engineering", "workspace setup", "pipeline design", "infrastructure provisioning", "Delta Lake patterns", "Spark development", "lakehouse configuration", "organize lakehouse tables", "create Livy session", "notebook deployment".
Exactly-once processing semantics with distributed coordination for file-based data pipelines. Atomic file claiming, status tracking, and automatic retry with in-memory fallback.
Parse and extract clinical data from HL7v2 messages and FHIR R4 resources using Python. Use when the user mentions HL7v2, HL7, FHIR, PID segment, OBX segment, MSH segment, Patient resource, Observation resource, Condition resource, MedicationRequest, EHR data extraction, clinical message parsing, FHIR bundle, HL7 to FHIR conversion, ADT message, ORU message, lab result extraction, or clinical data quality checks. Triggers include "parse HL7", "extract FHIR", "HL7v2 message", "FHIR resource", "PID segment", "OBX segment", "Patient resource", "Observation resource", "EHR parsing", "clinical data extraction", "HL7 to CSV", "FHIR to DataFrame".
This skill should be used when building data processing pipelines with CocoIndex, a Python library for incremental data transformation. Use when the task involves processing files/data into databases, creating vector embeddings, building knowledge graphs, ETL workflows, or any data pipeline requiring automatic change detection and incremental updates. CocoIndex is Python-native (supports any Python types), has no DSL, and uses version 1.0.0 or later.