Reference and coding guide for DataHub integration in backend development. Use when implementing or debugging code that touches DataHub — entities (datasets, dashboards, charts), aspects (ownership, tags, domains, glossary terms, structured properties), lineage, assertions, URNs, ingestion/emission, GraphQL, REST, or the acryl-datahub Python SDK. Also answers data-modeling questions and helps choose native DataHub features vs custom extensions. Trigger this skill whenever a task involves DataHub metadata, lineage, ingestion, or any DataHub API surface, even without explicit mention of "API".
Drive the helm-charts/bin/ install/uninstall/build/health scripts for both dev and prod profiles — configure, install, reinstall, uninstall, health-check, and rebuild-the-API / rebuild-the-frontend. The dev profile installs umbrella chart + peripherals (nginx-ingress, DataHub, Langfuse, dummy data, dev-lock) and auto-seeds peripheral connection config via the admin API. The prod profile installs the umbrella chart only; operator wires peripherals via /api/v1/admin/peripherals/*.
Walk a single api-wired UC scenario through the browser UI manually: for each step in the source test file, print the UI gesture (page + action), ask you to perform it and report what you observed, then independently probe the backend (REST read-back, DB rows, DataHub aspects, k8s secrets) to confirm the side effect fired. The human-driven sibling of the automated `tests/e2e/` use-case group — same scenarios, with a human at the browser. Optional argument `scope` selects the case (free-form: "UC1", "UC1 Case 2", "passive kafka", filename fragment, etc.).
Bidirectional sync between DataSpoke specs and implementation. Accepts a preset scope (prauto, ai-scaffold, k8s-deploy, helm-charts, api, ref, backend, frontend, all) or a free-form description of an area (e.g., "recently developed backend ingestion secret resolution"). Audits the scope, reports the gaps, asks the user which direction to resolve each gap (spec→impl, impl→spec, or leave-as-flagged), then applies the chosen edits. Trigger when specs and code may have drifted apart and the user wants them reconciled.
Download AI reference materials (external source code for AI assistant reference). Offers individual or all references.
Author timeless specification documents under spec/ (top-level or spec/feature/<FEATURE>.md) following the project hierarchy, naming conventions, and templates. Use when the user asks to write, design, or document a DataSpoke feature, component, or architectural decision as a reference spec. Do NOT use for implementation plans — those live in GitHub issues/PRs and are produced by native Plan mode or PRauto.
Connect this plugin to a deployed DataSpoke and verify access. Use to point Claude at a DataSpoke deployment (API base URL + dsk_ API token), mint a token from email/password, or check who you are and what role you hold. Prerequisite for every other dataspoke-* skill — run this first, or when calls start returning 401.
Manage DataSpoke validation (UC2) and author validation routines into your own data pipeline. Use to register/edit a dataset's validation slot, post or query results, browse the cross-dataset list — and, the flagship capability, to generate a validation routine (compute metrics, fetch baseline, forecast, decide score, POST result) into the engineer's pipeline script. Triggers on "add a validation routine", "validate table X", "register validation metrics".