Skills for the Bob+ IBM Technology Building Blocks

This collection of Skills for IBM Bob provides IBM Bob with the expertise to quickly build applications using the Bob+ IBM Technology Building Blocks. Each skill focuses on a specific Building Block and contains task-specific instructions, code patterns, examples and constraints Bob should follow when doing engineering work.

The Building Blocks are a community effort. Learn more about contributing your Skills for Bob+ IBM Technology Building Blocks.

How to install the Skills

The Skills have been packed into a single .zip that you can easily download and install. Go to the skills.zip page and click the Download raw file icon at the upper-right of the page. Copy all skill folders at either the global, ~/.bob/skills, or project-level, <project>/.bob/skills

Skill Taxonomy

Each Skill for IBM Building Blocks often aligns with an IBM product but not always. For specifics on how each skill works, read through the associated SKILL.md.

AI Skills
Agents	Agent Skills Build and deploy enterprise-ready AI agents that automate business workflows, orchestrate complex tasks, and accelerate software development through intelligent automation.
Govern	Real-Time Guardrails Add runtime safety and quality guardrails to Gen AI, RAG agents, and watsonx Orchestrate tools using watsonx.governance, Pass/Flag/Block at input, retrieval, generation, and output. Agent Ops Evaluate, red-team, and observe watsonx Orchestrate agents with the ADK evaluation framework (ADK 2.18+) on SaaS or Developer Edition — ground-truth test cases with handoff goals, metric diagnosis, rubrics, the attack catalogue, and platform traces. Model Evaluation Evaluate GenAI models and applications — prompts, RAG pipelines, LLM outputs, agentic tool-calling — using watsonx.governance metrics. Cost Management Measure, explain, and reduce the cost of watsonx Orchestrate agents — tokens and dollars from platform traces, the Langfuse integration and model pricing, a five-layer cost report with cost per successful journey, and optimization levers with their re-test.

Data Skills
Streamhouse	Data-streaming: Confluent Works with IBM Confluent for real-time data streaming, Kafka topic management, Schema Registry contracts, stream processing configuration, and event pipeline setup. Data-streaming: Confluent plus Terraform Expert guidance for building real-time streaming systems on Confluent Cloud using Infrastructure-as-Code (Terraform), Apache Flink SQL, and Python producers. Streamhouse Continuous RAG Combines Real-Time live operational state with RAG enterprise knowledge — design and implement continuous RAG pipelines that keep AI agents grounded in live context.
Pipelines	RAG Pipeline Builder Complete RAG pipeline design — IBM watsonx.ai embedding integration, OpenSearch HNSW + hybrid search design, chunking strategy selection, and evaluation with RAGAS metrics. RAG MCP Server Builder MCP server development (SSE transport, FastMCP), RAG ingestion + retrieval tool design, IBM Bob / Claude integration, and deployment to IBM Code Engine. Data Ingestion: Unstructured (UDI + Docling) IBM Docling document parsing, UDI pipeline configuration, multi-format chunking (PDF, DOCX, HTML, images), metadata extraction, and Python automation scripts. Data Ingestion: UDI + OpenSearch IBM UDI + OpenSearch integration, document search pipeline setup, and OpenSearch index provisioning for UDI output into IBM watsonx.data. Text2SQL: Metadata Enrichment watsonx.data Intelligence project onboarding, table/column description enrichment, synonym design, query example authoring, and accuracy measurement. Text2SQL: Query Optimizer Model selection, SQL safety validation, accuracy evaluation (exact-match + execution accuracy), error pattern diagnosis, and SQL dialect tuning. Data Ingestion: Structured (DataStage) IBM DataStage connector config, CDC pipeline design, schema mapping, batch and incremental load strategies into IBM watsonx.data lakehouse tables.
Lakehouse	Data Quality: Rules Data quality rule authoring, watsonx.data Intelligence quality checks, profiling automation, threshold design, and compliance reporting patterns for AI-ready data. Data Lineage: OpenLineage Instrumentation OpenLineage event design, Python/DataStage/Spark instrumentation patterns, IBM Databand lineage API integration, and lineage graph authoring. Data Observability: Databand Pipeline Setup IBM Databand pipeline onboarding, OpenLineage event design (START / COMPLETE / FAIL), alert policy authoring (null-rate, schema-drift, SLA-breach), and IAM auth patterns. Zero-Copy Lakehouse Presto SQL optimization, Spark job configuration on watsonx.data, cross-catalog federation design, and Iceberg table maintenance. Vector Search: AstraDB Astra DB vector collection creation, IBM watsonx.ai embedding integration, ANN cosine search queries via `astrapy` Data API, and metadata filtering. Vector Search: OpenSearch IBM watsonx.data OpenSearch k-NN index design, HNSW parameter tuning (`ef_construction`, `m`), hybrid search (vector + BM25) score fusion, and IBM watsonx.ai embedding integration.

Automation Skills
Build	Using the IBM Cloud CLI; ibmcloud Work with IBM Cloud by using the stand-alone `ibmcloud` CLI or IBM Cloud Shell. Infrastructure-as-code: Ansible Use for any Ansible-related tasks including playbook development, shell script conversion, debugging failures, or interactive setup. This is the parent skill that provides access to specialized Ansible workflows. Infrastructure-as-code: Terraform Use when writing, reviewing, or debugging Terraform/OpenTofu modules, tests, CI/CD pipelines, or state operations. Diagnoses failure modes (identity churn, secrets, blast radius, CI drift, state corruption) with version-aware guidance. Code Modernization Expert Modernize legacy code using enterprise patterns, automated refactoring, technical debt analysis, and incremental migration with zero downtime. Maximo Code Optimization Modernize and optimize Maximo automation scripts by analyzing legacy code patterns, identifying performance bottlenecks, and applying best practices for script efficiency. Transforms outdated automation scripts into maintainable, performant code while preserving business logic and ensuring compatibility with current Maximo versions. Maximo Java Conversion Convert legacy Maximo Java classes to automation scripts (Python/Jython, JavaScript, Nashorn, ECMAScript, MBR). Preserves business logic, generates test scripts, enforces MXLoggerFactory error handling and MboSet lifecycle patterns, and produces before/after conversion reports.
Optimize	Automated Resource Management (ARM): Turbonomic Automates application resource management at scale with the precision required to assure application performance. It continuously analyzes and optimizes compute, storage, and network resources in real time, helping organizations improve application resiliency, maximize infrastructure utilization, reduce operational costs, and ensure applications always receive the resources.
Secure	Non-human Identity: Vault Coming soon