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ibm-watsonx-data-integration-skills
ibm-watsonx-data-integration-skills contains 5 collected skills from IBM, with repository-level occupation coverage and site-owned skill detail pages.
Skills in this repository
API spec for pyflow, IBM's LLM-optimized Python DSL for authoring DataStage and StreamSets flows. Its compact surface and compile-time validation offer context efficiency, fast feedback, and correctness guarantees, making it the ideal choice for writing flows from scratch and for editing — bootstrap any structural change (a new source, a join) here before precision-editing the SDK.
Q&A reference for the DataStage parallel engine — parallelism, partitioning theory, APT configuration files, concurrent job execution, restart/recovery, disk/resource tuning, dataset performance, flow optimization (partitioning/sorting/memory), and per-stage semantics. Use for conceptual engine questions and stage property lookups regardless of authoring tool.
Reference for the verbose watsonx.data integration SDK for DataStage (batch) flows, with exhaustive stage and property access. New flows can only be created with pyflow's compact DSL (`di-agent-flow-pyflow`); use this SDK to edit existing flows in place — for what pyflow can't express.
Generates a Markdown bug report for an IBM watsonx.data integration session. User can invoke directly. The agent may propose it (and must wait for explicit acceptance) only after exhausting recovery options on a failure. Skip for non-watsonx.data integration sessions.
Reference for StreamSets Data Collector engines and StreamSets environments — StreamSets environment configuration, StreamSets engine deployment (Docker/Podman), StreamSets job execution, StreamSets engine communication methods (tunneling/direct), StreamSets high availability and failover, StreamSets monitoring and resource management. Use ONLY when the user explicitly mentions StreamSets, Data Collector, or a StreamSets-specific concern.