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Nw Document

Organization
nWave-ai
nw-document

Creates evidence-based documentation following DIVIO/Diataxis principles. Use when writing tutorials, how-to guides, reference docs, or explanations.

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-document
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 Document 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-document
  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-document .claude/skills/nw-document
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Document 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 Document 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 Document 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.

NW-DOCUMENT: DIVIO Documentation Creation

Wave: CROSS_WAVE | Agent: Orchestrator (self) Agents: Nova (nw-researcher)|Scholar (nw-researcher-reviewer)|Quill (nw-documentarist)|nw-documentarist-reviewer

Overview

Create evidence-based, DIVIO-compliant documentation by orchestrating research and writing phases with peer review at each gate. Cross-wave capability for any nWave phase.

Context Files Required

  • .nwave/trusted-source-domains.yaml — Embed inline in researcher prompt
  • ~/.claude/agents/nw/nw-researcher.md — Extract research methodology
  • ~/.claude/agents/nw/nw-documentarist.md — Extract DIVIO framework and templates

Command Syntax

bash
/nw-document [topic] [--type=tutorial|howto|reference|explanation] [--research-depth=overview|detailed|comprehensive|deep-dive]

If --type omitted, ask user. If --research-depth omitted, auto-select: tutorial->overview|howto->detailed|reference->comprehensive|explanation->deep-dive.

Orchestration Phases

Sub-agents have no Skill tool access. Embed all domain knowledge inline in each Task prompt. Read from agent files and config at orchestration time.

Phase 1: Research           @nw-researcher
Phase 1.5: Research Review  @nw-researcher-reviewer
Phase 2: Documentation      @nw-documentarist
Phase 2.5: Doc Review       @nw-documentarist-reviewer
Phase 3: Handoff

Phase 0: Pre-Flight

  1. Validate topic non-empty, type/depth valid if provided
  2. If type not specified, present DIVIO selection: TUTORIAL ("Teach me")|HOW-TO ("Help me do X")|REFERENCE ("What is X?")|EXPLANATION ("Why is X?")
  3. Determine output: docs/{tutorials|howto|reference|explanation}/{topic-kebab-case}.md
  4. Read and cache: .nwave/trusted-source-domains.yaml|nw-researcher.md|nw-documentarist.md

Phase 1: Research (@nw-researcher)

Invoke via Task tool. Prompt includes: topic|doc type|research depth|complete .nwave/trusted-source-domains.yaml (inline)|type-specific research focus (from nw-researcher.md)|quality gates: trusted sources only, 3+ sources/claim, citation coverage >95%, source reputation >=0.80. Output: docs/research/{topic-kebab-case}-for-{type}-doc.md

Phase 1.5: Research Review (@nw-researcher-reviewer)

Prompt includes: research artifact path|review focus (source verification|bias detection|evidence quality|cross-reference|doc readiness)|quality gates (same as Phase 1)|output: append YAML review metadata. Verdicts: APPROVED|NEEDS_REVISION|REJECTED

Phase 2: Documentation (@nw-documentarist)

Prompt includes: topic|type|research path|DIVIO framework principles (from nw-documentarist.md)|type-specific validation rules + template|collapse detection anti-patterns|quality gates: type purity >=80%, Flesch 70-80, zero spelling errors, zero broken links, style >=95%. Output: doc file + .validation.yaml

Phase 2.5: Documentation Review (@nw-documentarist-reviewer)

Prompt includes: doc artifact path|review focus (classification accuracy|validation completeness|collapse detection|recommendation quality|scores|verdict)|quality gates (same as Phase 2)|output: append YAML review metadata. Verdicts: APPROVED|NEEDS_REVISION|RESTRUCTURE_REQUIRED

Phase 3: Handoff

Verify all deliverables exist and both reviews APPROVED. Present: research path|doc path|validation path|quality gate results|review outcomes and iteration count.

Review Iteration Protocol

Applies to both review gates (Phase 1.5 and 2.5):

  1. APPROVED: Proceed to next phase
  2. NEEDS_REVISION: Extract critiques, re-invoke producer with feedback, re-invoke reviewer. Max 2 cycles. Escalate to user after 2 failures.
  3. REJECTED / RESTRUCTURE_REQUIRED: Escalate immediately — restart|adjust scope|accept with issues|cancel.

Examples

Example 1: Tutorial with auto-depth

bash
/nw-document "Getting Started with nWave" --type=tutorial

Orchestrator auto-selects overview depth, invokes researcher, reviews, invokes documentarist, reviews, outputs to docs/tutorials/getting-started-with-nwave.md.

Example 2: Explanation with explicit depth

bash
/nw-document "Why nWave Uses Hexagonal Architecture" --type=explanation --research-depth=deep-dive

Full 4-phase pipeline with deep-dive research. Output to docs/explanation/.

Example 3: Interactive type selection

bash
/nw-document "Mikado Method Integration"

Orchestrator prompts user to select DIVIO type before proceeding.

Success Criteria

  • Research created with trusted sources and cross-references
  • Research review: APPROVED (max 2 cycles)
  • Documentation follows DIVIO template with >=80% type purity
  • Doc review: APPROVED (max 2 cycles)
  • All deliverables exist: research|documentation|validation report
  • No collapse anti-patterns detected

Error Handling

  • Insufficient sources: continue with gaps|expand scope|cancel
  • Review iteration limit exceeded: escalate with persistent issues
  • Collapse detected: split into separate docs|revise scope|accept

Next Wave

Handoff To: Invoking workflow Deliverables:

  • Research: docs/research/{topic}-for-{type}-doc.md
  • Documentation: docs/{type-dir}/{topic-kebab-case}.md
  • Validation: docs/{type-dir}/{topic-kebab-case}.md.validation.yaml

Frequently asked questions

What does the Nw Document AI skill do?

Creates evidence-based documentation following DIVIO/Diataxis principles. Use when writing tutorials, how-to guides, reference docs, or explanations.

Why use Nw Document on TypingMind?

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

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

Which AI models can use Nw Document?

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 Document?

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

Is the Nw Document 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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