Build, run, and operationalize data flows in IBM watsonx.data integration. Describe your data requirements in natural language and let the agent plan, build, validate, and run the flow.
Plugin ibm-agentic-data-integration from marketplace ibm-watsonx-data-integration in IBM/ibm-watsonx-data-integration-skills. Commands read from the repo's manifest — 17 days ago.
di-agent-bug-reportGenerates a Markdown bug report for an IBM watsonx.data integration session. User can invoke directly. The agent MUST propose it (and must wait for explicit acceptance) after 3 or more consecutive calls to the same tool or the same sequence of tools result in a failure or the same error. Skip for no
di-agent-datastage-investigationDeep investigation of a failed run of a DataStage job already in operation — a scheduled or production job, or any job whose failure the user asks you to investigate. Works end-to-end from MCP tools: logs, job config, flow structure, connection health. Produces a root-cause diagnosis, not a fix. NOT
di-agent-flow-datastageReference for the verbose watsonx.data integration SDK for DataStage (batch) flows, with exhaustive stage and property access — the full engine stage catalog. Load this only after the di-agent-flow-lifecycle router has selected the datastage-sdk authoring backend — this is a syntax and stage referen
di-agent-flow-lifecycleEntry point for ALL flow work in watsonx.data integration — creating, editing, running, diagnosing a failed run, or backing up/restoring a flow. Load this FIRST whenever a request touches a data flow. It gathers intent — including which engine the flow runs on — routes to exactly one lifecycle state
di-agent-flow-pyflowAPI 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. Load this only after the di-agent-flow-lifecycle router has selected the pyflow autho
di-agent-knowledge-engine-datastageQ&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 ques
di-agent-parameter-setsGuide for creating, editing, and using parameter sets (paramsets) in IBM watsonx.data integration. Covers parameter types, value sets, PROJDEF, local parameters, runtime parameters, flow parameters, #param# and ${param} reference syntax for both DataStage (batch) and StreamSets (streaming) flows, an
Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data
9 skills · 9 commands · 11 agents · 1 MCP server
Automates browser interactions for web testing, form filling, screenshots, and data extraction
12 skills
Install the "AAS Data Analytics & Business Intelligence" workflow plugin from Agentic Awesome Skills.
16 skills
Databricks skills for the CLI, Apps, Lakebase, Model Serving, Lakeflow Jobs, Spark Declarative Pipelines, Declarative Automation Bundles (DABs), and classic-to-serverless migration.
37 skills · 2 commands · hooks
The official CCXT MCP server plus the ccxt-mcp usage skill — market data, balances and opt-in trading across 100+ crypto exchanges and prediction markets, with keys kept local.
1 MCP server
Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
10 skills · 2 commands · 3 agents
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