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Okf

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scaccogatto
okf

Author, maintain, and consume Open Knowledge Format (OKF) knowledge bundles — portable markdown + YAML frontmatter that both humans and agents read. Use when capturing project knowledge (services, APIs, schemas, metrics, runbooks, decisions) into an OKF bundle, when updating one after code or docs change, or when a repository contains an `.okf/` (or other OKF) bundle that should inform the task. Triggers on: "document this in OKF", "update the knowledge bundle", "capture this as a concept", or any work in a repo that has an OKF bundle.

Overview

Publisherscaccogatto
Repositoryokf-skills
Skill nameokf
Stars
424
Forks
40
Bundled files
7
LicenseMIT
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.

  • 7 bundled files

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

  • Open source

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

Installation

Install the Okf 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.
    https://github.com/scaccogatto/okf-skills/tree/main/skills/okf
  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/scaccogatto/okf-skills.git /tmp/okf-skills
mkdir -p .claude/skills
cp -r /tmp/okf-skills/skills/okf .claude/skills/okf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Okf 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 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 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) skill

OKF represents knowledge as a directory of markdown files with YAML frontmatter. It is minimal by design: no schema registry, no runtime, no SDK. Your job is to produce, maintain, and consume OKF bundles conformant with the spec, not your memory of it.

Always read the canonical spec before non-trivial work: reference/SPEC.md. It is the verbatim OKF v0.2 specification and the source of truth for every rule below.

The one hard rule

A bundle is conformant (§11) iff: every non-reserved .md file has a parseable YAML frontmatter block, and every such block has a non-empty type field. Everything else is soft guidance. Consumers MUST tolerate missing optional fields, unknown types, and broken links — never reject a bundle over them.

Conventions to apply

  • One concept = one file. The file path (minus .md) is the concept ID.
  • Frontmatter: type is required. Add title, description, tags when they aid consumption; add resource (a canonical URI) only for concepts bound to a real asset — omit it for abstract concepts.
  • Body: prefer structural markdown (headings, tables, lists, fenced code). Conventional headings: # Schema, # Examples, # Computation.
  • Cross-links: standard markdown links; prefer absolute bundle-relative form (/services/auth-api.md). A link asserts a relationship; its kind lives in the surrounding prose, not the link.
  • Reserved files: index.md (directory listing, no frontmatter, except the bundle-root index, which may carry okf_version and this plugin's upkeep: enforced opt-in flag, plus recursive: true (or a depth n: levels below the root, true is 6) to make the hook watch nested git repos) and log.md (ISO-dated history, newest first). Never use these names for concepts.
  • What goes in log.md: lifecycle events only, written in the bundle-root log.md: a bundle created, a concept deprecated or retired, one concept superseding another, concepts regenerated after their source changed, a verification pass. Routine edits to a concept do not get an entry: its generated and git already record them. A log is append-only, so never rewrite or drop an existing entry.

The v0.2 families (all optional, all worth filling)

  • Trust (§5.2): generated: { by, at } — who produced the current content and when. verified: [{ by, at }] — who confirmed it since (a bare mapping is one entry). Write by in the actor convention (§7): <producer>/<version> for an agent, human:<id> for a person, process:<id> for an automated job. Use human: whenever a person authored or signed off — consumers key trust tiers off that prefix.
  • Lifecycle (§5.4–5.5): status: draft|stable|deprecated (absent means stable) and stale_after, an absolute ISO 8601 datetime (a bare date is tolerated), not a TTL.
  • Provenance (§5.1): sources: [{ id, resource, title, author, usage_count, last_modified }] plus a usage_window: { from, to } sibling of sources framing every usage_count (an entry may carry its own to override it); a usage_count without a window warns. resource is required per entry and may be a URL, a bundle path, or a scope descriptor. Attribute a specific claim with a markdown footnote whose label is the source's id: …sharded daily.[^ga4-schema] plus a [^ga4-schema]: … definition. The label is the join key, it must match a sources[].id.
  • Attestation (§10): a sanctioned computation is its own concept, type: Attested Computation, carrying runtime (required), parameters, executor, attester, and the computation itself under # Computation (or a computation: path). Concepts that need the value link to it. Never inline a number's SQL into the concept that narrates it.

Reading a v0.1 bundle? Two constructs were superseded (§13.1): timestamp is now generated.at, and a body # Citations list is now sources. Read both, write v0.2 — and when you touch a legacy concept in maintain mode, migrate its frontmatter as part of the edit. The validator warns on both.

Templates to copy: concept, index, log.

Default bundle location

Use .okf/ at the repository root unless the project already uses another location. Commit it alongside the code it describes — knowledge as code.

Modes

produce — create or extend a bundle

Starting a brand-new bundle? Use the init fast-path instead of hand-writing the first files — it scaffolds a conformant index.md, log.md, and a getting-started.md concept with full recommended frontmatter in one shot:

bash
uv run "${CLAUDE_SKILL_DIR}/scripts/okf_init.py" <target-dir> [--title "..."]

It refuses to touch a directory that already has .md files unless --force is given. Then extend it:

  1. Read reference/SPEC.md.
  2. Pick the source(s): code (derive concepts from source, READMEs, docstrings, config), docs/wiki (distill pages into concepts, record the originals in sources), manual (decisions, playbooks, metrics).
  3. Choose a directory layout by domain (e.g. services/, datasets/, decisions/). One concept per file.
  4. Write each concept from templates/concept.md: set a descriptive type, fill recommended fields, record generated and the sources you actually read, cross-link related concepts.
  5. Add/refresh index.md per directory (and okf_version: "0.2" in the root index). A new bundle gets a dated Creation entry in log.md. Extending an existing one does not.
  6. Validate (see below). Fix every error before finishing.

maintain — keep a bundle in sync with reality

  1. Identify which concepts the change affects (search by resource, path, or topic). This bookkeeping is exactly what agents are good at — touch every affected file in one pass.
  2. Update the body and generated.at (with your own actor in generated.by); fix or add cross-links; create new concepts for new assets; mark removed assets status: deprecated and note the deprecation in log.md rather than silently deleting context. Facing a whole v0.1 bundle rather than a stray field? Do not hand-edit it — run the validator's --migrate once.
  3. Update the relevant index.md files. Append a dated log.md entry only if the change is a lifecycle event (see What goes in log.md).
  4. Validate.

consume — use a bundle as context

  1. Read the bundle-root index.md first for progressive disclosure, then follow links only into the concepts relevant to the task.
  2. Weigh what you read: status: draft/deprecated, a stale_after already past, or no verified entry all mean "check before relying on this". Treat broken links as not-yet-written knowledge, not errors.
  3. Need a number an Attested Computation covers? Run its computation with values bound to the declared parameters — never write your own query.
  4. If you learn something durable while working, switch to maintain and write it back.

Validation (do this before declaring done)

Never eyeball conformance — run the deterministic checker. Invoke the companion validate skill (/okf:validate <bundle-dir> --strict), which ships the checker. If that skill is not installed, run it directly:

bash
uv run "${CLAUDE_SKILL_DIR}/../validate/scripts/okf_validate.py" <bundle-dir> --strict

Resolve every ERROR (hard §11 failures). Under --strict warnings also fail the run (exit 1), so clear them too; drop --strict (or use --max-warnings N) if you only want §11 conformance gated.

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 AI skill do?

Author, maintain, and consume Open Knowledge Format (OKF) knowledge bundles — portable markdown + YAML frontmatter that both humans and agents read. Use when capturing project knowledge (services, APIs, schemas, metrics, runbooks, decisions) into an OKF bundle, when updating one after code or docs change, or when a repository contains an `.okf/` (or other OKF) bundle that should inform the task. Triggers on: "document this in OKF", "update the knowledge bundle", "capture this as a concept", or any work in a repo that has an OKF bundle.

Why use Okf on TypingMind?

Because you install it once and use it with any model. Okf 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 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scaccogatto/okf-skills/tree/main/skills/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?

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?

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

Is the Okf AI skill free?

Yes. It is published on GitHub by scaccogatto under the MIT 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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