Atlas / Skills / nwave-ai / Nw Diverger Review Criteria

Nw Diverger Review CriteriaSAFE

skills/nwave-ai/nw-diverger-review-criteria

AI agents that guide you from idea to working code, with you in control at every step.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
617
01

Overview

AI agents that guide you from idea to working code, with you in control at every step.

Read from source at commit 1a864ec81cdeOBSERVED · 2026-10-14
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02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: nw-diverger-review-criteria
description: Review criteria for the nw-diverger-reviewer — validates JTBD rigor, research quality, option diversity, taste application correctness, and recommendation coherence in DIVERGE wave artifacts
user-invocable: false
disable-model-invocation: true
---

# 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 refer
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-14 · audit v0.4.1 · source sha 1a864ec81cdefull audit observations/trust-audit/skill/nwave-ai__nw-diverger-review-criteria.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-141a864ec81cdeSAFEB89first audit
05

Questions

What does the Nw Diverger Review Criteria skill do?

AI agents that guide you from idea to working code, with you in control at every step.

Is Nw Diverger Review Criteria safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Nw Diverger Review Criteria access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (1a864ec81cde), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.

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