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Found 201 Skills
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
Use when designing multi-tenant OCI environments, setting up production landing zones, implementing compartment hierarchies, or establishing governance foundations. Covers Landing Zone reference architectures, compartment strategy, network topology patterns (hub-spoke vs multi-VCN), IAM structure, tagging standards, and cost segregation.
Use when defining events, fields, and governance for GTM analytics pipelines.
Boomi platform help — enterprise iPaaS, 1000+ connectors, API Management, Data Hub MDM, Flow low-code builder, Event Streams, B2B/EDI, AgentStudio AI agents, MCP support. Use when Boomi integration keeps failing or data isn't syncing, connector won't authenticate to SAP or Salesforce, per-connection pricing is spiraling and you need to optimize, debugging a Boomi process is painful with vague error messages, evaluating Boomi vs MuleSoft vs Workato, or setting up API management and governance. Do NOT use for simple Zapier/Make automations (use /sales-integration) or MuleSoft-specific questions (use /sales-mulesoft).
Expert patterns for Segment Customer Data Platform including Analytics.js, server-side tracking, tracking plans with Protocols, identity resolution, destinations configuration, and data governance best practices. Use when: segment, analytics.js, customer data platform, cdp, tracking plan.
Use when conducting user research (interviews, usability tests, surveys, A/B tests) or designing research studies. Covers discovery, validation, evaluative methods, research ops, governance, and measurement for software experiences.
Manage Harness Software Supply Chain Assurance (SSCA) via MCP. Configure automated SBOM generation with CycloneDX or SPDX formats, set up artifact signing and attestation with Cosign, define supply chain security policies using OPA, and track SLSA provenance levels. Use when asked to generate SBOMs, sign artifacts, enforce supply chain policies, track software provenance, or manage SLSA compliance. Do NOT use for OPA pipeline governance policies (use create-policy instead) or vulnerability scanning (use security-report instead). Trigger phrases: SBOM, software bill of materials, supply chain security, SLSA, artifact signing, cosign, provenance, attestation, CycloneDX, SPDX, supply chain policy.
Recommends and manages DevOps Center test suite assignments for pipeline stages. Mode A analyzes a commit diff against assigned suite metadata to recommend relevant existing suites and flag coverage gaps (pure reasoning). Modes B-D assign a single suite, bulk-map multiple suites with a mandatory impact preview, or add/remove test classes with governance rules, via the testSuiteStages Connect API. Use this skill to recommend suites for a commit, assign or map suites to stages, or add/remove tests in a suite. TRIGGER when: the user asks which suites to run for a commit/diff or what covers their changes; a suite is unlinked and the user wants it assigned; the user wants to configure suite-to-stage mappings, assign multiple suites, or add/remove/sync tests in a suite. DO NOT TRIGGER when: configuring or syncing a test provider (use dx-devops-test-pipeline-configure), running suites (use dx-devops-test-suite-run), or authoring/running tests directly (use platform-apex-test-generate or platform-apex-test-run).
Push Packer build metadata to HCP Packer registry for tracking and managing image lifecycle. Use when integrating Packer builds with HCP Packer for version control and governance.
Data lake and lakehouse platform patterns: ingestion/CDC, transformations, open table formats (Iceberg/Delta/Hudi), query and serving engines (Trino/ClickHouse/DuckDB), orchestration, governance/lineage, cost and operations. Self-hosted and cloud options.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Use to design and document customer segments with clear criteria, metrics, and governance.