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CSV OKF Connector
This skill provides a Go-based CLI tool to document a directory of CSV files as an Open Knowledge Format (OKF) bundle: one concept per CSV, with an inferred column schema and an optional data profile, and to sync enriched descriptions back to a .okf-metadata.yaml sidecar.
When to Use
Use this skill when you need to:
- Catalog a folder of CSV files — each becomes a
CSV Fileconcept undertables/,
with a # Columns table whose types are inferred by sampling values.
- Capture per-column statistics (
--profile) and sample rows (--sample) as
grounding for enrichment.
- Round-trip enriched descriptions back to the source via
.okf-metadata.yaml.
CSV has no declared types or comment store, so column types are inferred and descriptions live in the sidecar (the same pattern okf-fs uses).
Setup
# Install the published binary (Go 1.24+):
go install github.com/xSAVIKx/okf-skills/skills/okf-csv@latest
# …or build from a clone:
cd skills/okf-csv && go build -o okf-csv .How to Use
1. Produce an OKF bundle
./okf-csv produce --dir <csv-directory> --out <bundle-dir> [--sample <N>] [--profile]--dir(required): directory of CSV files (traversed recursively, honoring.okfignore).--out(required): output OKF bundle directory.--sample <N>: embed up to N sample rows per file as a## Samplesection.--profile: compute per-column statistics (non-null, null, distinct, min, max,
detected semantic type, low-cardinality value sets) into a ## Data Profile section.
Each data/orders.csv becomes tables/orders.md. Re-running preserves unchanged concepts byte-for-byte (incremental produce), keeping any enriched descriptions.
2. Ingest / sync descriptions
./okf-csv ingest --dir <csv-directory> --bundle <bundle-dir> [--sync]- Verifies each concept's columns still match its CSV header (reports drift).
--sync: writes the bundle's descriptions back to.okf-metadata.yamlin--dir.
3. Inspect the schema (self-description)
./okf-csv schemaPrints the machine-readable JSON description used by okf-mcp to expose the skill.

