Note: This is an independent community showcase. We are not affiliated with the original OKF creators. Visit the official site.

Portable knowledge bundles for humans and AI agents.

OKF v0.1 YAML frontmatter Markdown body Git-friendly

OKF represents the metadata, context, and curated insight around data and systems as linked Markdown concept files with YAML frontmatter. Human-readable, agent-parseable, zero platform lock‑in.

v0.1

Format version

7

Copy-ready examples

3

Hard requirements

metrics/monthly-recurring-revenue.md

OKF Concept File

Core Concepts

Three building blocks

OKF is intentionally minimal. Everything builds on bundles, concepts, and links.

Knowledge Bundles

A bundle is a folder you distribute – Git repo, ZIP, or tarball. An index.md maps the contents, a log.md records changes.

Concept Files

One Markdown file per concept. YAML frontmatter with at least type. The file path (minus .md) becomes the concept ID.

Linked Concepts

Standard Markdown links assert relationships. The prose around the link explains what kind. A # Citations section tracks external sources.

Interactive Explorer

Browse a real OKF bundle

Click files in the tree to inspect concept files. Linked concepts are highlighted.

SaaS Metrics Bundle

index.md

markdown

Use Cases

Bundles for every domain

OKF fits curated knowledge that should be read by both humans and agents.

Metrics

SaaS Metrics

Metrics, source tables, dashboards, and review playbooks as linked concepts.

okf/ ├── index.md ├── metrics/mrr.md ├── tables/subscriptions.md └── playbooks/revenue-review.md

APIs

API Documentation

Endpoints, schemas, errors, and auth without replacing OpenAPI.

okf/ ├── index.md ├── endpoints/create-customer.md ├── schemas/customer.md └── errors/rate-limit.md

Framework

Laravel App

Routes, models, policies, jobs, and operational context for humans and agents.

okf/ ├── index.md ├── models/user.md ├── routes/api-users.md └── policies/user-policy.md

CMS

WordPress Site

Custom post types, taxonomies, ACF fields, and templates as portable knowledge.

okf/ ├── index.md ├── post-types/product.md ├── acf/product-fields.md └── templates/single-product.md

Data

Data Warehouse

Datasets, tables, metrics, lineage, and data quality notes near the systems they describe.

okf/ ├── index.md ├── datasets/sales.md ├── tables/orders.md └── metrics/gross-revenue.md

AI

Agent Context

Give agents durable context about systems, tools, playbooks, and constraints before they act.

okf/ ├── index.md ├── systems/billing.md ├── tools/stripe-api.md └── constraints/rate-limits.md

Parseability

Built for both worlds

Humans get readable documents. Agents get structured JSON.

Human View

Markdown Rendered

Monthly Recurring Revenue

Metric • [revenue, saas, finance]


Calculation
Sum of active subscription amounts.

Agent View

JSON Parsed

{ "type": "Metric", "title": "Monthly Recurring Revenue", "tags": ["revenue", "saas"], "body": "# Calculation\\nSum of active subscription amounts." }

Ecosystem

3 lines of code

No specialized SDK required. Use the Markdown libraries you already know.

Python

frontmatter package

import frontmatter # Parse the OKF file concept = frontmatter.load('okf/metrics/mrr.md') # Access metadata and body print(concept['type']) # -> 'Metric' print(concept.content) # -> '# Calculation...'

JavaScript / Node

gray-matter package

const matter = require('gray-matter'); // Parse the OKF file const concept = matter.read('okf/metrics/mrr.md'); // Access metadata and body console.log(concept.data.type); // -> 'Metric' console.log(concept.content); // -> '# Calculation...'

Live Playground

Write & validate OKF

Edit YAML frontmatter and Markdown. See the parsed result and validation status in real time.

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Comparison

OKF vs. adjacent standards

OKF doesn't replace these tools – it fills the knowledge layer gap between them.

Feature OKF RAG Chunks OpenAPI JSON-LD Wiki / Docs
Human-readable
Agent-parseable
Structured metadata
Cross-concept links
Git-friendly
No server required
Domain-agnostic
Portable (ZIP / Git)

FAQ

Frequently Asked Questions

Why not just use plain Markdown?
Plain Markdown is great for humans, but terrible for agents. Without structured metadata (like YAML frontmatter), agents struggle to confidently categorize concepts or extract properties like resource URIs and tags.
Do I need a special parser?
No! OKF uses standard YAML frontmatter and standard Markdown syntax. Any existing library that parses frontmatter (like gray-matter for JS, or python-frontmatter) will parse an OKF file perfectly.
How does this differ from RAG or vector databases?
OKF is a storage format, not an execution engine. You can parse OKF bundles into a RAG pipeline or a vector database. OKF simply gives you a standardized way to author, version, and share the knowledge before it gets vectorized.

Getting Started

Six steps to your first bundle

No SDK, no platform. Just folders, frontmatter, and Markdown.

01

Create the bundle folder

Add an okf/ directory in your repo, project, or export location.

mkdir okf
02

Add a root index

List your concept groups so humans and agents can scan the bundle at a glance.

# okf/index.md --- okf_version: "0.1" --- # Metrics * [MRR](metrics/mrr.md)
03

Create a concept file

One Markdown file per concept. Give it YAML frontmatter with at least a type.

# okf/metrics/mrr.md --- type: Metric title: Monthly Recurring Revenue --- # Calculation Sum of active subscription amounts.
04

Add metadata

Enrich with description, resource, tags, and timestamp when useful.

description: Recurring revenue normalized monthly. resource: dashboard://revenue/mrr tags: [revenue, saas, finance] timestamp: 2026-06-13T00:00:00Z
05

Link related concepts

Use standard Markdown links. Add # Citations for external sources.

MRR is calculated from the [subscriptions table](/tables/subscriptions.md). # Citations [1] [Revenue dashboard](dashboard://revenue/mrr)
06

Ship it

Commit the folder, ship as an archive, or expose it to an agent or catalog.

git add okf/ git commit -m "Add OKF knowledge bundle" git push

Join the OKF Community

Help shape the standard, build parsers, or share your knowledge bundles. We're building a common language for humans and AI.

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