Okf Visualize Knowledge

OKF Visualize Knowledge

Problem

Knowledge bases grow organically as folders of markdown files with cross-references. Without visualization:

• Hard to spot which concepts are isolated (zero cross-refs)
• Hard to see which skills have the most evidence
• Hard to communicate "here's what I know" to teammates
• Manual diagrams are stale the moment you write them

Reality: A 5-second command turns your notes into an interactive graph.

Context / Trigger Conditions

Use this skill when:

• You have a directory of .md files with at least one cross-link ([text](other.md))
• The files have a consistent organization (subdirectories, or flat)
• You want to audit which concepts are orphans
• You want a shareable, offline HTML view

Don't use:

• Single-file notes (no cross-refs → no graph)
• Confidential knowledge (the HTML has all content embedded)

Solution: 5-Minute Pipeline

Step 1: Install OKF tools

git clone https://github.com/GoogleCloudPlatform/knowledge-catalog.git
cd knowledge-catalog/okf
pip install -e .        # installs enrichment-agent CLI

Step 2: Add YAML frontmatter (optional but recommended)

Without frontmatter, OKF treats every file as a "Document" with no type. Add minimal frontmatter:

---
type: Skill
title: My Skill Name
description: One-line summary.
tags: [skill, kaggle]
timestamp: 2026-06-17T00:00:00Z
---

# My Skill

Body text with [cross-links](other-file.md)...

Step 3: Add index.md to each subdirectory (optional)

Each subdirectory gets a hub page:

---
type: Index
title: Subdir Name
description: What lives here.
---

# Subdir Name

* [file-one](file-one.md)
* [file-two](file-two.md)

Step 4: Run visualize

enrichment-agent visualize \
  --bundle /path/to/your/notes \
  --out /tmp/my-graph.html

Output: a self-contained HTML file (~50 KB) you can open or share.

Step 5: Iterate

Add a new concept → re-run visualize → see how the graph grew. Orphan concepts (no cross-refs) become obvious in the graph — fix them by adding links.

What OKF Generates

Wrote 12 concept(s), 35 edge(s), 64117 bytes → /tmp/my-graph.html

• concept(s): markdown files with at least one frontmatter or body content
• edge(s): markdown links between files (counted as edges)
• bytes: HTML output size (~5 KB per concept)

Type-Driven Coloring

The viewer colors nodes by type: frontmatter field:

Use descriptive, self-explanatory type values — consumers (visualize, future readers) handle unknown types gracefully.

Validation

docs/ml-agent-memory/ is the working example:

• 12 concepts / 35 edges / 64 KB HTML
• 4 subdirectories (competitions/, experiments/, skills/, lessons/)
• 8 different type values used
• Renders in < 5 seconds on M-series Mac

cd ~/projects/cultivating-ml-agent/docs/ml-agent-memory
enrichment-agent visualize --bundle . --out /tmp/viz.html
open /tmp/viz.html

Why OKF vs Alternatives

OKF wins when you want a vendor-neutral format with git-versioned history and a single static-HTML viewer.

Real-World Usage

Validated during S6E2 AutoGluon rerun (2026-06-14):

• Discovered ml-agent-memory had 0 OKF edges before migration
• Added frontmatter + cross-links → jumped to 6/24 in 10 minutes
• Subsequent S6E2 lesson extractions → 12/35 in another 30 minutes

Each new concept added at < 5 minutes total (write frontmatter + cross-link to existing concepts).

Notes

Type values are free but pick descriptive ones. The OKF repo's issue #60 proposes ML-canonical types (Experiment, Model, Submission, Lesson, Competition, Bundle). Use those for ML knowledge; pick your own for other domains.

File-relative links only. The OKF viewer rewrites absolute paths to GitHub-friendly relative paths (PR #45 in OKF repo).

Related Skills

• kaggle-data-format-first — verify data format BEFORE research (similar "verify first" philosophy)
• autogluon-first — get a strong baseline in 15 min before iterating
• ml-sweet-spot — when to stop optimizing

References

• GoogleCloudPlatform/knowledge-catalog — OKF source
• OKF v0.1 SPEC
• PR #6 in topprismdata/cultivating-ml-agent — OKF migration of docs/ml-agent-memory/