OKF Bundle Enrichment Guidance Skill

This skill teaches an AI agent (Claude Code, Cursor, Gemini CLI, Copilot, …) how to enrich an Open Knowledge Format (OKF) bundle — adding or improving the human-readable description of each concept (table, dataset, file, directory) — using the agent's own LLM.

There is deliberately no binary and no embedded model here. Generating a good description is a judgment task, and the harness driving the project already has a capable LLM in the loop. Embedding a second one would mean a model calling a tool that calls another model: redundant cost, an extra API key to manage, and usually a worse result than the model already doing the work. So enrichment is delivered as guidance — the procedure and the quality bar — for whatever LLM is present, exactly as okf-reader is guidance for reading a bundle.

When to Use

Load this skill when asked to enrich, document, describe, annotate, or "improve the descriptions in" an OKF bundle — typically after a connector has produced the bundle and before syncing descriptions back to the source.

Pairs with:

• okf-reader — follow its rules to read and navigate the bundle efficiently (index-first, frontmatter-only when possible, grep for targeted lookups).
• the connectors (okf-sqlite, okf-mysql, okf-postgresql, okf-bigquery, okf-fs, okf-git) — the producers and the sync target. Enrichment is far better when the bundle was produced with --profile and --sample (the four SQL connectors), and the descriptions you write can be pushed back to the origin with the connector's ingest --sync.

The OKF concept document

Each concept is a markdown file with YAML frontmatter: