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Okf Knowledge Base

OrganizationPopular
inkeep
okf-knowledge-base

Open Knowledge Format (OKF) v0.2 guidance. Use when creating, reading, reviewing, or maintaining an OKF bundle; responding to OpenKnowledge `okf` plugin warnings; or choosing types, provenance, links, indexes, or logs.

Overview

Publisherinkeep
Repositoryopen-knowledge
Skill nameokf-knowledge-base
Stars
4.2K
Forks
279
Bundled files
1
LicenseGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by inkeep on GitHub. Read the source before you install it.

Installation

Install the Okf Knowledge Base AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/inkeep/open-knowledge.git /tmp/open-knowledge
mkdir -p .claude/skills
cp -r /tmp/open-knowledge/packages/server/assets/skills/packs/okf .claude/skills/okf-knowledge-base
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Okf Knowledge Base in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Okf Knowledge Base on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Okf Knowledge Base is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Open Knowledge Format (OKF)

OKF v0.2 is a portable format for agent-readable knowledge: Markdown files, YAML frontmatter, and standard links. The /open-knowledge skill governs tool use; this skill covers OKF semantics.

Core rules

  • A bundle is a directory tree of .md files. Each non-reserved file is one concept; its path without .md is its ID.
  • Every concept needs parseable frontmatter with a non-empty string type. No other field is always required.
  • Types are an open vocabulary. Consumers must accept unfamiliar types and metadata.
  • Use standard Markdown links for portable relationships. Broken links and a missing index are allowed.
  • index.md and log.md are reserved at every level. Use lowercase filenames.
  • An index.md normally has no frontmatter; only the root index may declare okf_version: "0.2".
  • A log.md is newest-first; entry headings begin with an ISO date — ## YYYY-MM-DD: Summary (the summary after the date is optional; a bare ## YYYY-MM-DD is equally conformant).
  • OKF consumers read .md, not .mdx.

Authoring judgment

  • Make each concept the smallest useful link or citation target. Choose a stable, descriptive type; Document is only a generic fallback.
  • Do not invent facts, relationships, resources, sources, verification, or history. Missing knowledge is better than false structure.
  • Use title, description, resource, and tags only when they add real information.
  • Record provenance in sources. Join claim-level citations with matching sources[].id and Markdown footnotes.
  • Keep authorship and verification separate: generated says who produced content; verified says who confirmed it. Use exact lowercase human: and process: prefixes when applicable.
  • Write every provenance timestamp as an ISO 8601 datetime with an explicit UTC offset (stale_after: 2026-12-31T00:00:00Z), never a bare date and never an offsetless time. This covers generated.at, verified[].at, stale_after, sources[].last_modified, and both usage_window bounds. A log.md entry heading is different: it stays a plain YYYY-MM-DD date.
  • Treat status: deprecated and expired stale_after values as trust signals, not validation errors.
  • For type: Attested Computation, follow the declared runtime and parameters. Do not rewrite the sanctioned computation.

Read and maintain a bundle

  • Start with the nearest index.md, inspect frontmatter, then follow only relevant links.
  • Prefer current, verified sources, but tolerate unknown types and incomplete links.
  • If the bundle conflicts with an assumption, trust the bundle; if it is missing or inconsistent, say so.
  • Write durable discoveries back to the relevant concept and authored enumerations.
  • Add a truthful dated log.md entry after durable changes when the bundle uses a log.
  • Read legacy timestamp and body citations, but prefer v0.2 generated.at and sources when updating a concept. Never invent provenance while migrating.

OpenKnowledge's okf plugin

The optional project plugin provides continuous portability feedback without blocking writes:

  • Write-time warnings and project audits check structure, frontmatter, reserved files, links, and .mdx use.
  • .ok/okf/*.schema.json contains the precise field contracts. Read these generated files instead of guessing; do not edit them.
  • Deterministic lint findings establish conformance. Agent judgment still establishes whether metadata is true and useful.
  • Optional index generation maintains index.md files. Generated indexes are machine-owned: never edit them, because OpenKnowledge replaces their contents.
  • log.md remains authored, not generated.

The plugin is off by default and each rule can be disabled. Its value is early warning when OpenKnowledge-native content would be misread by another OKF consumer.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Okf Knowledge Base AI skill do?

Open Knowledge Format (OKF) v0.2 guidance. Use when creating, reading, reviewing, or maintaining an OKF bundle; responding to OpenKnowledge `okf` plugin warnings; or choosing types, provenance, links, indexes, or logs.

Why use Okf Knowledge Base on TypingMind?

Because you install it once and use it with any model. Okf Knowledge Base is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Okf Knowledge Base in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/inkeep/open-knowledge/tree/main/packages/server/assets/skills/packs/okf. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Okf Knowledge Base?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Okf Knowledge Base?

As many as you like. As long as a model supports skills, you can use Okf Knowledge Base with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Okf Knowledge Base AI skill free?

Yes. It is published on GitHub by inkeep under the GPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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