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Nw Divio Framework

Organization
nWave-ai
nw-divio-framework

DIVIO/Diataxis four-quadrant documentation framework - type definitions, classification decision tree, and signal catalog

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-divio-framework
Stars
616
Forks
65
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Divio Framework 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/nWave-ai/nWave/tree/main/nWave/skills/nw-divio-framework
  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/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-divio-framework .claude/skills/nw-divio-framework
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Divio Framework 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 Nw Divio Framework 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 Nw Divio Framework 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.

DIVIO Documentation Framework

The Four Quadrants

Exactly four documentation types. Each serves one purpose. Never mix.

Tutorial

Orientation: Learning | Need: "Teach me" | Key Q: Can newcomer follow without external context? Purpose: enable first success | Assumption: user knows nothing | Format: step-by-step guided experience Success: gains competence + confidence | Include: safe repeatable steps, immediate feedback, building blocks Exclude: problem-solving, assumed knowledge, comprehensive coverage

How-to Guide

Orientation: Task | Need: "Help me do X" | Key Q: Achieves specific, measurable outcome? Purpose: accomplish specific objective | Assumption: baseline knowledge, needs goal completion Format: focused steps to outcome | Success: task completed Include: clear goal, actionable steps, completion indicator | Exclude: teaching, background, all scenarios

Reference

Orientation: Information | Need: "What is X?" | Key Q: Factually complete and lookup-ready? Purpose: accurate lookup | Assumption: user knows what to look for | Format: structured, concise, factual Success: finds correct info quickly | Include: complete API/function details, parameters, returns, errors Exclude: narrative, tutorials, opinions

Explanation

Orientation: Understanding | Need: "Why is X?" | Key Q: Explains reasoning and context? Purpose: conceptual understanding | Assumption: user wants "why" | Format: discursive, reasoning-focused Success: understands design rationale | Include: context, reasoning, alternatives, architectural decisions Exclude: step-by-step, API details, task completion

Classification Matrix

                  PRACTICAL           THEORETICAL
STUDYING:         Tutorial            Explanation
WORKING:          How-to Guide        Reference

Adjacent: Tutorial/How-to (both have steps, differ in assumed knowledge) | How-to/Reference (both "at work") | Reference/Explanation (both knowledge depth) | Explanation/Tutorial (both "studying")

Classification Decision Tree

START: What is the user's primary need?

1. Is user learning for the first time?
   YES -> TUTORIAL
   NO  -> Continue

2. Is user trying to accomplish a specific task?
   YES -> Does it assume baseline knowledge?
         YES -> HOW-TO GUIDE
         NO  -> TUTORIAL (reclassify)
   NO  -> Continue

3. Is user looking up specific information?
   YES -> Is it factual/lookup content?
         YES -> REFERENCE
         NO  -> Likely EXPLANATION
   NO  -> Continue

4. Is user trying to understand "why"?
   YES -> EXPLANATION
   NO  -> Re-evaluate (content may need restructuring)

Classification Signals

Tutorial Signals

Positive: "Getting started", "Your first...", "Prerequisites: None", "What you'll learn", "Step 1, Step 2...", "You should see..." Red flags: "Assumes prior knowledge", "If you need to...", "For advanced users..."

How-to Signals

Positive: "How to [verb]", "Before you start" (with prerequisites), "Steps", "Done:" or "Result:" Red flags: "Let's understand what X is...", "First, let's learn about..."

Reference Signals

Positive: "API", "Parameters", "Returns", "Throws", "Type:", Tables of functions/methods Red flags: "This is probably...", "You might want to...", Conversational tone

Explanation Signals

Positive: "Why", "Background", "Architecture", "Design decision", "Trade-offs", "Consider", "Because" Red flags: "1. Create...", "2. Run...", "Step-by-step", "Do this:"

JTBD-First Resolution (When in doubt, ask the JTBD)

Provenance: Ale 2026-05-02. "La JTBD ci dice quale need stiamo soddisfando."

When the four-types decision tree is ambiguous, OR when a stylistic decision inside a chosen type has plausible alternatives (ordering, fold-vs-keep, scratch-vs-real-data, prose-vs-table, etc.), DO NOT pick by aesthetic preference. Return to the document's JTBD and let the answer fall out.

Each Diataxis type maps to a canonical JTBD:

TypeCanonical JTBDImplied constraints
Tutorial"Build my mental model of X by doing it once, end-to-end, without risking my real project state."Reproducible, safe, self-contained, cause-effect visible
How-to"Fix this specific situation right now; don't teach me fundamentals."Problem-statement first, decision tree, no preamble, link forward to Tutorial for newcomers
Reference"Look up a specific answer (flag, exit code, schema field, error message)."ctrl-F friendly, dense, alphabetical/systematic, no narrative, separate H2 per lookup category
Explanation"Understand the design rationale so I can extend or critique it."Discursive, links to evidence (probes, RCAs, ADRs), no how-to instructions, no command examples beyond the minimum needed for context

Application: resolving a stylistic dispute

Worked example, Tutorial Q: scratch directory vs use the project's seeded data?

  • JTBD: "Build mental model end-to-end without risking my real project state."
  • Constraint surfaced: "without risking" → seeded data DOES risk contamination → scratch wins.
  • Constraint surfaced: "end-to-end" → cause-effect visible → seeded data hides cause-effect under pre-existing entries → scratch wins.

Worked example, Reference Q: fold three short sections into one "Notes"?

  • JTBD: "Look up a specific answer."
  • Constraint surfaced: "specific answer" → ctrl-F friendly → 3 distinct H2 entries are 3 distinct landing points → folding hurts the JTBD.
  • Verdict: keep separate.

Worked example, README "Learn More" Q: alphabetical or semantic grouping?

  • JTBD on a README is "I'm new; show me docs in the order I should read them" — pedagogical browsing, NOT lookup.
  • Constraint surfaced: "in the order I should read them" → semantic grouping (install adjacent, authoring adjacent, troubleshooting last) → wins.
  • Alphabetical serves a DIFFERENT JTBD ("I know the doc name") which has another surface (file system / search).

Decision protocol

When facing a stylistic ambiguity inside a chosen type:

  1. Restate the document's JTBD in one sentence (use the canonical JTBD as the starting point, refine if the doc is more specific).
  2. List the constraints implied by that JTBD (be concrete: "reproducible", "ctrl-F friendly", "links to evidence").
  3. Compare each candidate option against the constraint list.
  4. Pick the option satisfying the most constraints. If tied, pick the option that fails the FEWEST constraints (loss-aversion).

This procedure replaces aesthetic debate with a falsifiable check.

Anti-pattern: collapse via JTBD-blending

Some authors blend two JTBDs in one document ("a Tutorial that's also a Reference"). This is the same collapse pattern the four-types-only rule rejects, surfaced via the JTBD lens. Detection: if the document has TWO canonical JTBDs in tension (e.g. "build mental model" AND "look up specific answer"), flag for splitting.

Frequently asked questions

What does the Nw Divio Framework AI skill do?

DIVIO/Diataxis four-quadrant documentation framework - type definitions, classification decision tree, and signal catalog

Why use Nw Divio Framework on TypingMind?

Because you install it once and use it with any model. Nw Divio Framework 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 Nw Divio Framework in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-divio-framework. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Divio Framework?

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 Nw Divio Framework?

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

Is the Nw Divio Framework AI skill free?

Yes. It is published on GitHub by nWave-ai 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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