---
name: nwave-ai-nw-diverge
description: "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."
license: "MIT"
metadata:
  decimalai-skill-id: "7d0e2d68-477b-4941-8df9-8b23c97707d2"
  decimalai-version-id: "b59e5b42-42e3-46c3-a6bb-522f4b697d47"
  decimalai-version: "2"
  decimalai-body-sha256: "a88d37059bdb81e3aed5fdfacb215830e8dfd2674ee7ec145d37fd86f098de4e"
  decimalai-name: "nwave-ai/nw-diverge"
  decimalai-format: "1"
  decimalai-invocation: "model"
  decimalai-category: "research"
  decimalai-source: "https://app.decimal.ai/s/nwave-ai-nw-diverge@2/SKILL.md"
---
# NW-DIVERGE

## Authority

DIVERGE updates the durable `docs/product/jobs.yaml` (the validated job,
opportunity scores, changelog). It never writes a per-delivery decision
ledger. `recommendation.md` and its supporting artifacts are the ephemeral
workspace read while this delivery is open, not a second authority.

## Required Pass

1. **Job (JTBD)** -- extract and elevate the job from the request or
   `docs/product/vision.md`/DISCOVER evidence, at strategic or physical
   level, not tactical. Produce functional/emotional/social statements and
   minimum 3 ODI outcome statements.
2. **Competitive research** -- invoke `nw-researcher` for evidence-grounded
   mapping of how existing products serve the job, including at least one
   non-obvious alternative.
3. **Brainstorming** -- frame the HMW question, apply SCAMPER, generate 6
   structurally diverse options (mechanism, assumption and cost differ) by
   stating a probability per candidate and sampling the tails; name each
   option with the requester's words where they exist and mark coined names.
4. **Taste evaluation** -- apply the DVF filter, score surviving options on
   the 4 locked-weight taste criteria, produce a traceable ranking with a
   dissenting case for the second-place option.
5. **Peer review** -- `nw-diverger-reviewer` validates all 5 dimensions,
   maximum 2 revision iterations.

## Handoff

Update `docs/product/jobs.yaml` once with the validated job and a changelog
entry referencing this feature-id. Return the recommendation and decision
statement; do not author a `wave-decisions.md` or any other per-wave ledger.

```text
DIVERGE-RESULT
verdict: PASS | NEEDS_INPUT
job: <JOB-id, statement>
options: <count generated, count survived>
recommended: <direction phrased by mechanism in the requester's vocabulary> -- <one-line rationale>
dissent: <second-place direction phrased by mechanism> -- <why it might win under different assumptions>
ssot: <docs/product/jobs.yaml updated>
```

The recommended and dissent lines use the mechanism in the requester's
vocabulary; coined handles and their name_origin stay in the scoring table
inside taste-evaluation.md.

## Human-readable authority

Return changed feature decisions to the Product Owner rather than editing a
generated brief. Use current full briefs and HTML when useful; `jobs.yaml` is
not itself a generated feature brief. Read `nw-doc-as-artifact` for human review.
No wave-entry preference or expansion offer is required.