Loading...
Loading...
Found 78 Skills
Use when user explicitly asks Flink/Ververica/Realtime Compute Console workspace operations: 草稿(draft), SQL校验/执行, 部署(deployment), 作业(job), Session Cluster, namespace, 表(table), 成员(member), 变量(variable), 或 checkpoint timeout 诊断, especially with workspace/deployment/job IDs (w-*, d-*, j-*, sc-*, draft-*). Also use when prompt asks to test/verify Flink Console lifecycle flow, safety guardrails, or parameter validation for these operations. This includes prompts such as create draft, deploy draft, list deployments, start/stop job, create/list session cluster, get tables, list variables. Also use when prompt explicitly asks to run `python scripts/flink_ververica_ops.py` for Flink Console workspace operations. Do not trigger for unrelated "workspace" contexts or generic cloud/platform tasks (ECS, OSS, RDS, Kafka, Spark, Kubernetes, billing, weather). Do not trigger for Flink instance lifecycle operations (create/scale/delete/renew); those belong to alibabacloud-flink-instance-manage.
Routes PubNub events to external systems with no code via Events & Actions (E&A). Covers event listeners (Messages, Users, Channels, Push, Memberships), action targets (Webhook, SQS, Kinesis, S3, Kafka, IFTTT, AMQP), filter types (basic vs JSONPath), retry policy, envelopes, and batching. Use when integrating PubNub with Lambda, Kafka, SQS, S3, EventBridge, an analytics pipeline, or any external system.
Use this skill when a user wants to store, manage, or work with Goldsky secrets — the named credential objects used by pipeline sinks. This includes: creating a new secret from a connection string or credentials, listing or inspecting existing secrets, updating or rotating credentials after a password change, and deleting secrets that are no longer needed. Trigger for any query where the user mentions 'goldsky secret', wants to securely store database credentials for a pipeline, or is working with sink authentication for PostgreSQL, Neon, Supabase, ClickHouse, Kafka, S3, Elasticsearch, DynamoDB, SQS, OpenSearch, or webhooks.
Design and architect Goldsky Turbo pipelines. Use this skill for 'should I use X or Y' decisions: kafka source vs dataset source, streaming vs job mode, which resource size (xs/s/m/l/xl/xxl) for my workload, postgres vs clickhouse vs kafka sink, fan-in vs fan-out data flow, one pipeline vs many, dynamic table vs SQL join, how to handle multi-chain deployments. Also use when the user asks 'what's the best way to...' for a pipeline design problem, or is unsure how to structure their pipeline before building it.
Build and deploy new Goldsky Turbo pipelines from scratch. Triggers on: 'build a pipeline', 'index X on Y chain', 'set up a pipeline', 'track transfers to postgres', or any request describing data to move from a chain/contract to a destination (postgres, clickhouse, kafka, s3, webhook). Covers the full workflow: requirements → dataset selection → YAML generation → validation → deploy. Not for debugging (use /turbo-doctor) or syntax lookups (use /turbo-pipelines).
Comprehensive plugin for SAP Datasphere development with 3 specialized agents, 5 slash commands, and validation hooks. Use when building data warehouses on SAP BTP, creating analytic models, configuring data flows and replication flows, setting up connections to SAP and third-party systems, managing spaces and users, implementing data access controls, using the datasphere CLI, creating data products for the marketplace, or monitoring data integration tasks. Covers Data Builder (graphical/SQL views, local/remote tables, transformation flows), Business Builder (business entities, consumption models), analytic models (dimensions, measures, hierarchies), 40+ connection types (SAP S/4HANA, BW/4HANA, HANA Cloud, AWS, Azure, GCP, Kafka, Generic HTTP), real-time replication, task chains, content transport, CLI automation, catalog governance, and data marketplace. Includes 2025 features: Generic HTTP connections, REST API tasks in task chains, SAP Business Data Cloud integration. Keywords: sap datasphere, data warehouse cloud, dwc, data builder, business builder, analytic model, graphical view, sql view, transformation flow, replication flow, data flow, task chain, remote table, local table, sap btp data warehouse, datasphere connection, datasphere space, data access control, elastic compute node, sap analytics cloud integration, datasphere cli, data products, data marketplace, catalog, governance
Goldsky Turbo pipeline YAML reference — the authoritative source for field names, required vs optional fields, and valid values. Use whenever the user asks about specific YAML fields: what does `start_at: earliest` vs `latest` do, what fields does a postgres/clickhouse/kafka sink require, what is the `from:` field in a sink, how does `checkpoint` work, what's the syntax for `batch_size` or `primary_key`. Also use for validation errors like 'unknown field' or 'missing required field'. For interactive pipeline building end-to-end, use /turbo-builder instead.
Answer questions using the Tenzir documentation. Use whenever the user asks about TQL syntax, pipeline operators, functions, data parsing or transformation, normalization, OCSF mapping, enrichment, lookup tables, contexts, packages, nodes, platform setup, deployment, configuration, integrations with tools like Splunk, Kafka, S3, Elasticsearch, or any other Tenzir feature. Also use when the user asks how to collect, route, filter, aggregate, or export security data with Tenzir, or needs help writing or debugging TQL pipelines, even if they don't mention 'Tenzir' explicitly but are clearly working in a Tenzir context.
Deploys infrastructure components via Helm charts on TrueFoundry. Supports any public or private OCI Helm chart including databases (Postgres, MongoDB, Redis), message brokers (Kafka, RabbitMQ), and vector databases (Qdrant, Milvus). Uses YAML manifests with `tfy apply`. Use when installing Helm charts or deploying infrastructure on TrueFoundry.
Event Sourcing, CQRS, Saga patterns, event bus (Kafka, RabbitMQ, AWS EventBridge). Use when implementing event-driven architecture, distributed transactions, or event sourcing.
Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
Event-driven architecture patterns including message queues, pub/sub, event sourcing, CQRS, and sagas. Use for async messaging, distributed transactions, event stores, domain/integration events, data streaming, choreography/orchestration, or integrating with Kafka, RabbitMQ, Pulsar, SQS/SNS, or NATS.