Nw Diverge logo

Nw Diverge

Organization
nWave-ai
nw-diverge

Generates 3-5 divergent design directions through JTBD analysis, competitive research, structured brainstorming, and taste evaluation before convergence. Use when the team has a validated problem but hasn't chosen a solution approach.

Overview

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

Use it in TypingMind

Enable Nw Diverge 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 Diverge 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 Diverge 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-DIVERGE: Structured Divergent Thinking Before Convergence

Wave: DIVERGE (between DISCOVER and DISCUSS, optional) | Agent: Flux (nw-diverger) | Command: /nw-diverge

Overview

Execute DIVERGE wave through Flux's 4-phase workflow: JTBD analysis|competitive research|structured brainstorming|taste-filtered evaluation. Transforms a validated problem into 3-5 concrete, taste-scored design directions so DISCUSS can converge on one with confidence.

DIVERGE is optional. Brownfield features with a clear direction may skip it (see skip checklist in design spec). New products and pivot decisions benefit most from structured divergence.

Interactive Decision Points

Decision 1: Work Type

Question: What type of work is this? Options:

  1. New product -- no prior solution exists, full divergence needed
  2. Brownfield feature -- existing product, exploring approach alternatives
  3. Pivot / redesign -- existing feature being reconsidered from scratch
  4. Other -- user provides custom context

Decision 2: Research Depth

Question: How deep should competitive research go? Options:

  1. Lightweight -- 3 competitors, known market
  2. Comprehensive -- 5+ competitors including non-obvious alternatives
  3. Deep-dive -- cross-category research, adjacent markets, academic references

Prior Wave Consultation

Before beginning DIVERGE work, read SSOT and prior wave artifacts:

  1. SSOT (if docs/product/ exists):
    • docs/product/jobs.yaml -- validated jobs and opportunity scores
    • docs/product/vision.md -- product vision and strategic context
  2. Project context: docs/project-brief.md | docs/stakeholders.yaml (if available)
  3. DISCOVER artifacts: Read docs/feature/{feature-id}/discover/ (if present)
    • wave-decisions.md -- validated assumptions and key decisions
    • problem-validation.md -- customer evidence grounding the problem

Migration gate: If docs/product/ does not exist but docs/feature/ has existing features, STOP. Guide the user to docs/guides/migrating-to-ssot-model/README.md and complete the migration first. If greenfield, DIVERGE will bootstrap docs/product/jobs.yaml with the validated job.

READING ENFORCEMENT: You MUST read every file listed in Prior Wave Consultation above using the Read tool before proceeding. After reading, output a confirmation checklist. Do NOT skip files that exist -- skipping causes options disconnected from evidence.

Agent Invocation

@nw-diverger

Execute *diverge for {feature-id}.

Context Files: see Prior Wave Consultation above + project context files.

Configuration:

  • work_type: {Decision 1}
  • research_depth: {Decision 2}
  • output_directory: docs/feature/{feature-id}/

SKILL_LOADING: Before starting work, load your skill files using the Read tool from ~/.claude/skills/nw-{skill-name}/SKILL.md. Skills encode your methodology -- without them you operate with generic knowledge only.

At the start of execution, create these tasks using TaskCreate and follow them in order:

  1. JTBD Analysis — Load jtbd-analysis skill. Extract and elevate the job from the raw request or DISCOVER evidence. Produce job statements (functional + emotional + social) and ODI outcome statements. Gate: job at strategic or physical level (not tactical), minimum 3 ODI outcome statements produced.
  2. Competitive Research — Invoke nw-researcher sub-agent for evidence-grounded competitive research. Map how existing products serve the validated job. Identify non-obvious alternatives. Gate: 3+ real competitors named, at least one non-obvious alternative, evidence quality confirmed.
  3. Brainstorming — Load brainstorming skill. Frame HMW question, apply SCAMPER lenses, generate structurally diverse options. Gate: 6 options generated with diversity confirmed (mechanism, assumption, and cost structure differ across options).
  4. Taste Evaluation — Load taste-evaluation skill. Apply DVF filter, score surviving options on 4 taste criteria with locked weights, produce weighted ranking and recommendation with dissenting case. Gate: all surviving options scored on all 4 criteria, recommendation traceable to scoring matrix, dissenting case documented.
  5. Peer Review — Invoke nw-diverger-reviewer (Prism) to validate all 5 dimensions. Revise if needed (max 2 iterations). Gate: reviewer approval confirmed, handoff accepted by nw-product-owner.

Success Criteria

  • Job extracted at strategic or physical level (not tactical, not a feature description)
  • Minimum 3 ODI outcome statements produced
  • 3+ real competitors researched, at least one non-obvious alternative
  • 6 structurally diverse options generated (different mechanism, assumption, cost)
  • All surviving options scored on all 4 taste criteria with locked weights
  • Recommendation traceable to scoring matrix (no "feels right" overrides)
  • Dissenting case documented for second-place option
  • Peer review approved by nw-diverger-reviewer
  • Handoff accepted by nw-product-owner (DISCUSS wave)

Next Wave

Handoff To: nw-product-owner (DISCUSS wave) Deliverables: recommendation.md with explicit decision statement + supporting DIVERGE artifacts

Wave Decisions Summary

Before completing DIVERGE, produce (or append to) docs/feature/{feature-id}/wave-decisions.md:

markdown
# DIVERGE Decisions -- {feature-id}

## Key Decisions
- [D1] {decision}: {rationale} (see: {source-file})

## Job Summary
- Validated job: {job statement at strategic/physical level}
- ODI outcomes: {count} outcome statements

## Options Evaluated
- {count} options generated, {count} survived DVF filter
- Recommended: {option name} -- {one-line rationale}
- Dissent: {second-place option} -- {why it might be better under different assumptions}

## SSOT Updates
- jobs.yaml: {created|updated} with job JOB-{NNN}

SSOT Update

After producing feature-level artifacts, update the product-level SSOT:

  1. Jobs SSOT: Create or update docs/product/jobs.yaml with the validated job from Phase 1. Add changelog entry referencing this feature-id.
  2. If docs/product/ does not exist, create the directory. This is the SSOT bootstrap.

SSOT files use schema_version and changelog fields. See canonical schema in the design spec.

Expected Outputs

Feature delta (in docs/feature/{feature-id}/)

  recommendation.md             (top 3 options, dissenting case, decision for DISCUSS)
  wave-decisions.md             (DIVERGE section appended)

Internal artifacts (in docs/feature/{feature-id}/diverge/)

  job-analysis.md               (validated job + ODI outcome statements)
  competitive-research.md       (prior art, competitor analysis, non-obvious alternatives)
  options-raw.md                (all generated options, unfiltered, no evaluation)
  taste-evaluation.md           (DVF filter, locked weights, scoring matrix)
  review.yaml                   (peer review result from nw-diverger-reviewer)

SSOT updates (in docs/product/)

  jobs.yaml                     (created or updated with validated job + changelog entry)

Examples

Example 1: New product divergence

/nw-diverge notification-system

DISCOVER artifacts present with validated problem. Flux reads problem-validation.md, extracts job ("minimize likelihood of developers missing critical failure signals"), researches 5 notification tools including non-obvious alternatives (ambient light signals, IDE annotations), generates 6 structurally diverse options, scores with taste evaluation, recommends proactive push with Slack integration. Updates jobs.yaml with validated job.

Example 2: Brownfield feature without DISCOVER

/nw-diverge rate-limiting

No DISCOVER artifacts. Flux works from project-brief.md and direct conversation. Extracts job via 5 Whys from "we need rate limiting" to strategic level. Creates docs/product/jobs.yaml (SSOT bootstrap). Proceeds through all 4 phases.

Example 3: Skip validation (DIVERGE not needed)

/nw-diverge --skip auth-bugfix

Skip checklist evaluated: clear direction exists (bugfix), no competing approaches, self-evident path. DIVERGE skipped. User directed to /nw-discuss or /nw-distill depending on work type.

Frequently asked questions

What does the Nw Diverge AI skill do?

Generates 3-5 divergent design directions through JTBD analysis, competitive research, structured brainstorming, and taste evaluation before convergence. Use when the team has a validated problem but hasn't chosen a solution approach.

Why use Nw Diverge on TypingMind?

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

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

Which AI models can use Nw Diverge?

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 Diverge?

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

Is the Nw Diverge 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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