Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/xsavikx/okf-skills --skill okf-csvAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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.
