| name | okf-csv |
| description | CSV connector that produces and ingests Open Knowledge Format (OKF) bundles from a directory of CSV files. Infers each file's column schema (integer/number/boolean/date/string) by sampling rows, optionally embeds a per-column data profile and sample rows, and syncs descriptions back to a .okf-metadata.yaml sidecar. Use when documenting or cataloging a folder of CSV/flat files, or capturing their inferred schema and stats as an OKF bundle. CGO-free, pure Go. |
| license | Apache-2.0 |
| compatibility | Requires the Go toolchain (1.24+) to build the connector binary. No CGO needed. |
| metadata | {"version":"0.2.0","author":"Yurii Serhiichuk","tags":"okf, knowledge-catalog, csv, flat-file, schema, profile"} |
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 File concept under tables/,
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
go install github.com/xSAVIKx/okf-skills/skills/okf-csv@latest
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 ## Sample section.--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.yaml in --dir.
3. Inspect the schema (self-description)
./okf-csv schema
Prints the machine-readable JSON description used by okf-mcp to expose the skill.
