| name | kg-etl |
| skill_type | skill |
| description | Runs a unified source→(ontological transform)→sink ETL flow over the KG hub — pull any connector in and/or load a write-back SoR, graph store, or engine SQL table out, with lineage. Use for cross-system data movement — "ETL servicenow into stardog", "list ETL sources/sinks", "show ETL lineage". |
| license | MIT |
| tags | ["graph-os","ingestion","etl"] |
| tier | core |
| metadata | {"author":"Genius","version":"0.1.0"} |
kg-etl
Condensed intent-surface note (Seam 8). Under the default intent surface (MCP_TOOL_MODE=intent), graph_etl is held back from the default tool list (nothing removed — REST + _execute_tool still reach it exactly as documented below). Two ways to use this skill unchanged: (1) load_tools(tools=["graph_etl"]) once per session (as below), then proceed exactly as documented; or (2) call the write intent verb with the same natural-language request — the resolver routes to graph_etl for you and returns the result plus a routing justification. Set MCP_TOOL_MODE=condensed/verbose/both to expose the granular tools eagerly instead.
graph_etl (CONCEPT:AU-KG.ontology.one-source) composes ingestion + write-back + graph-store machinery. action='run': pull source into the KG (any registered ingestion source; mode delta|full|reconcile) and/or load sink from the KG — sink is a write-back SoR (dry-run + approval, pass ops_json), a graph store (stardog/neo4j/age/jena_fuseki or a registered connection), or the native engine SQL table (sink='table'). action='list' shows sources/sinks/backends; action='lineage' shows recorded runs (AU-KG.ontology.kg-3).
Invoke
- MCP:
load_tools(tools=["graph_etl"]), then graph_etl(action="list").
- REST twin:
POST /graph/etl with {"action": "run", "source": "servicenow", "sink": "stardog", "mode": "delta"}.
Example
graph_etl(action="run", source="leanix", sink="table", ops_json='{"table":"apps","replace":true}')
Delegation
If graph-os is reachable, offload composite multi-step work via graph_orchestrate (execute_agent / execute_workflow) instead of hand-running the steps — let the local LLM + Loop engine do it, and resolve only the exceptions.
