nw-diverge

Solid

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.

AI & Automation 526 stars 55 forks Updated 1 weeks ago MIT

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Quality Score: 92/100

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Skill Content

# 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 ...

Details

Author
nWave-ai
Repository
nWave-ai/nWave
Created
3 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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