tabular-knowledge-bootstrap

To turn CSV or SQL export results into a structured taxonomy, this skill normalizes tabular evidence into a SQLite-backed discovery graph, generating taxonomy candidates and refining buckets across a seven-phase workflow to produce durable knowledge artifacts.

Building a discovery knowledge base from Excel sheets and JSON arrays involves normalizing structured inputs into a shared kernel, capturing source profiles, and promoting dimensions with representative examples to enable repeatable downstream collaboration.

Yes, this tabular knowledge bootstrap workflow supports JSON arrays and SQL exports alongside CSV, TSV, and Excel sheets, guiding the agent through structure profiling, taxonomy candidate generation, and projection planning.

To generate a downstream projection plan from raw tabular inventory data, the workflow promotes structured taxonomies by defining dimensions, collecting representative examples, and outputting a concrete mapping for tickets, monitoring, or access knowledge planes.

You need a seven-phase workflow for normalizing tabular data when you want to enforce structure, source profiling, taxonomy generation, bucket refinement, example selection, promotion, and projection to produce durable and reusable knowledge artifacts.