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Found 50 Skills
Creates new Robot Framework test cases for SnapLogic pipeline testing. Use when the user wants to create a new test file, add test cases to existing files, or needs test templates for specific system types (Oracle, PostgreSQL, Snowflake, etc.).
Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints from user, discovers existing connections and RDS/Redshift candidates, registers credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers on: connect to database, set up Glue connection, register data source, connect to Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection. Do NOT use for moving data (use ingesting-into-data-lake), creating tables (use creating-data-lake-table), queries (use querying-data-lake), catalog exploration (use exploring-data-catalog), or SaaS (Salesforce, ServiceNow, SAP, MongoDB, Kafka).
Cloud & AI FinOps advisory skill. Structured cost optimization using the FinOps Foundation framework. Covers AWS, Azure, GCP, OCI, AI inference, and data platforms (Databricks, Snowflake). Use for: cloud costs, cost optimization, cloud spend, AI costs, cloud bill, FinOps assessment, GreenOps, right-sizing, commitment strategy, tagging governance.
Serverless GDS sessions on Neo4j Aura — covers GdsSessions, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection, gds.graph.project.remote, gds.graph.construct, algorithm execution (mutate/stream/write), async job polling, result retrieval, and session lifecycle. Use when running graph algorithms on Aura Business Critical or VDC, processing graph data from Pandas/Spark, or using the graphdatascience Python client in AGA (serverless) mode. Covers all three data source three source modes (AuraDB-connected, self-managed Neo4j, standalone from DataFrames). Does NOT cover the embedded GDS plugin on Aura Pro or self-managed Neo4j — use neo4j-gds-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover Snowflake Graph Analytics — use neo4j-snowflake-graph-analytics-skill.
Plan a migration onto MotherDuck. Use when moving from Snowflake, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, validation, rollback, and native-versus-DuckLake posture.
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
Custom Apex adapter generation for Salesforce Connect — connects any external REST API to Salesforce as live, queryable External Objects without ETL or data copying. TRIGGER when: user wants to connect a non-standard external API to Salesforce, asks to write a DataSource.Provider or DataSource.Connection, wants to surface external data as Salesforce records, says "connect my API to Salesforce without copying data", or asks whether custom Apex is the right approach for any system. Also trigger on: "custom adapter", "ExternalId field", "DataSource namespace". DO NOT TRIGGER when: user is configuring a standard adapter they already have and is NOT asking about custom Apex (e.g., OData endpoint setup, Snowflake connector config). DO NOT TRIGGER when: user wants to copy or bulk-sync data — use platform-data-manage. DO NOT TRIGGER for Apex callouts outside Connect — use platform-apex-generate or integration-connectivity-generate.
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.
Iterable platform help — cross-channel customer engagement with Studio journey builder, AI suite (Brand Affinity, STO, Predictive Goals), and Smart Ingest from 23+ data sources. Use when configuring Studio journeys, setting up campaigns or experiments, managing email/SMS/push/in-app/WhatsApp channels, using Iterable AI features, configuring Smart Ingest or Snowflake sync, or troubleshooting Iterable. Do NOT use for general email marketing strategy (use /sales-email-marketing), push notification strategy (use /sales-push-notification), in-app messaging strategy (use /sales-in-app-messaging), transactional email strategy (use /sales-transactional-email), cross-platform deliverability (use /sales-deliverability), or connecting tools generically (use /sales-integration).
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.
Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.
Design bulk file and lake export as a B2B product surface - the two-camp choice between Parquet file-drops on object storage and native warehouse/lake sharing (Snowflake shares, Delta Sharing, linked datasets), the partitioning and schema-evolution contract across batches, delivery cadence and freshness commitments (full dump vs incremental vs CDC vs streaming), shared-bucket security with per-recipient credentials, egress cost allocation, and cross-border data residency. Use whenever the user mentions bulk export, data dumps to S3, Parquet, Snowflake shares, Delta Sharing, or export freshness SLAs - even if they never say "bulk data sharing". Do NOT use for live SQL endpoints - use samber/developer-platform-skills@sql-jdbc-access-design instead.