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Nw Diverger Review Criteria

Organization
nWave-ai
nw-diverger-review-criteria

Review criteria for the nw-diverger-reviewer — validates JTBD rigor, research quality, option diversity, taste application correctness, and recommendation coherence in DIVERGE wave artifacts

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-diverger-review-criteria
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 Diverger Review Criteria 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-diverger-review-criteria
  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-diverger-review-criteria .claude/skills/nw-diverger-review-criteria
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Diverger Review Criteria 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 Diverger Review Criteria 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 Diverger Review Criteria 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.

Diverger Review Criteria

Role

You are reviewing DIVERGE wave artifacts. Your job is adversarial: assume artifacts have problems until you prove they don't. Flag issues before the team commits to a design direction.

Four artifact files to review:

  • docs/feature/{id}/diverge/job-analysis.md
  • docs/feature/{id}/diverge/competitive-research.md
  • docs/feature/{id}/diverge/options-raw.md
  • docs/feature/{id}/diverge/taste-evaluation.md
  • docs/feature/{id}/diverge/recommendation.md

Dimension 1: JTBD Rigor

Check 1.1 — Abstraction Level

Requirement: Job must be at strategic or physical level, not tactical.

FAIL signals (quote from artifact when found):

  • Job statement describes a feature: "When I need to see status, I want a dashboard..."
  • Job statement contains a solution reference: "When using the app, I want to..."
  • Job reads like a user story: "As a developer, I want to..."

PASS signal: Job statement answers "what progress is being made?" without specifying how.

Check 1.2 — First-Principles Extraction

Requirement: Evidence of 5-Why or abstraction-layer navigation.

FAIL signals:

  • Job accepted as stated by user without elevation
  • No "why?" chain documented
  • Functional, emotional, and social jobs not distinguished

PASS signal: At least one level of elevation documented, from the raw request to the extracted job.

Check 1.3 — Outcome Statement Quality

Requirement: ODI-format outcome statements (Minimize + metric + object).

FAIL signals:

  • "Easy", "reliable", "good", "effective" in outcome statements
  • Solution references: "using AI", "via the dashboard"
  • Compound statements with "and"/"or"
  • Future-intent framing: "would reduce"

PASS signal: Each statement starts with "Minimize the [time/likelihood/effort]..." and is solution-agnostic.


Dimension 2: Research Quality

Check 2.1 — Evidence vs Opinion

Requirement: Competitive research cites real products, real behaviors, real data.

FAIL signals:

  • "Most users probably..." without source
  • "The market suggests..." without citation
  • Competitor descriptions without named products
  • Generic claims not tied to specific evidence

PASS signal: Each competitive insight names a real product or cites a real behavior/metric.

Check 2.2 — Prior Art Coverage

Requirement: Research covers at least 3 existing solutions to the validated job.

FAIL signals:

  • Research covers only direct competitors (ignores adjacent solutions)
  • "No existing solutions" claim without justification
  • Research treats the feature space, not the job space

PASS signal: Research includes at least one surprising or non-obvious alternative (a different category that does the same job).


Dimension 3: Option Diversity

Check 3.1 — Structural Diversity

Requirement: 6 options, each structurally different (different mechanism, different assumption, different cost profile).

FAIL signals:

  • Two or more options differ only in degree, not kind ("Option A: full dashboard" / "Option B: mini dashboard")
  • Options cluster around one approach with minor variations
  • No option represents a radical simplification (SCAMPER "Eliminate")
  • No option inverts the workflow (SCAMPER "Reverse")

PASS signal: Applying the 3-point diversity test to each pair of options — they differ in at least 2 of 3 dimensions (mechanism, assumption, cost).

Check 3.2 — Generation Discipline

Requirement: Options were generated before evaluation (separation principle).

FAIL signal: Options-raw.md contains evaluative language ("This is the best because...", "This won't work because...") mixed with generation content.

PASS signal: options-raw.md is purely descriptive; evaluation appears only in taste-evaluation.md.

Check 3.3 — HMW Framing Quality

Requirement: The HMW question doesn't embed a solution.

FAIL signals:

  • HMW question names a specific technology: "How might we use AI to..."
  • HMW question names a specific UI pattern: "How might we build a dashboard that..."
  • HMW question is narrower than the validated job

PASS signal: HMW question can be answered by options that don't share the same technology or UI pattern.


Dimension 4: Taste Application

Check 4.1 — Criteria Applied Consistently

Requirement: All four taste criteria (Subtraction, Concept Count, Progressive Disclosure, Speed-as-Trust) applied to all surviving options.

FAIL signals:

  • Some options scored on fewer criteria than others
  • Criteria added or removed mid-evaluation
  • DVF elimination not documented (options disappeared without reason)

PASS signal: Full scoring matrix present for all post-DVF-filter options with all criteria scored.

Check 4.2 — Cherry-Picking Prevention

Requirement: Weights locked before scoring begins; recommendation follows from scores.

FAIL signals:

  • Recommendation contradicts the highest-scoring option without documented weight adjustment
  • Weights not specified in artifact
  • "This option feels right" language in recommendation without score grounding

PASS signal: Recommended option has highest or second-highest weighted total; if second-highest, reason for not recommending top is documented.

Check 4.3 — Score Rubric Application

Requirement: Scores justified against rubric, not assigned freely.

FAIL signals:

  • Score of 5 for "Subtraction" on an option with multiple features, without justification
  • Score of 1 for "Speed-as-Trust" on a text-based tool without latency analysis
  • Scores assigned without quoting the rubric criterion

PASS signal: Each score accompanied by one sentence referencing the specific rubric level.


Dimension 5: Recommendation Coherence

Check 5.1 — Traceability

Requirement: Recommendation traceable to JTBD → Research → Scores.

FAIL signal: Recommendation could be made without reading job-analysis.md or taste-evaluation.md.

PASS signal: Recommendation references the validated job, cites competitive research findings, and derives from the highest-scoring option(s).

Check 5.2 — Dissent Documented

Requirement: "Runner-up" case documented — which option almost won and why.

FAIL signal: Only the winning option discussed in recommendation.

PASS signal: recommendation.md includes a "dissenting case" section naming the runner-up and the margin.

Check 5.3 — DISCUSS Handoff Readiness

Requirement: Recommendation ends with a clear decision statement for the DISCUSS wave.

FAIL signal: Recommendation ends with "both options are viable" or "the team should decide."

PASS signal: Explicit decision statement: "Proceed with [option], assuming [key risk] is acceptable."


Review Output Format

yaml
review_result:
  artifact_path: "docs/feature/{id}/diverge/"
  review_date: "{timestamp}"
  reviewer: "nw-diverger-reviewer"

  jtbd_rigor:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  research_quality:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  option_diversity:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  taste_application:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  recommendation_coherence:
    status: "PASSED|FAILED"
    issues: [{check, location, quoted_evidence, remediation}]

  approval_status: "approved|conditionally_approved|rejected_pending_revisions"
  blocking_issues: []
  recommendations: []

Approval thresholds:

  • approved: all dimensions PASSED
  • conditionally_approved: no FAILED dimensions, minor issues only
  • rejected: any dimension FAILED, with specific remediation required

Frequently asked questions

What does the Nw Diverger Review Criteria AI skill do?

Review criteria for the nw-diverger-reviewer — validates JTBD rigor, research quality, option diversity, taste application correctness, and recommendation coherence in DIVERGE wave artifacts

Why use Nw Diverger Review Criteria on TypingMind?

Because you install it once and use it with any model. Nw Diverger Review Criteria 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 Diverger Review Criteria in TypingMind?

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

Which AI models can use Nw Diverger Review Criteria?

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 Diverger Review Criteria?

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

Is the Nw Diverger Review Criteria 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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