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Nw diverge

Skill nWave-ai/nWave/nWave/skills/nw-diverge

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

Install
npx -y skills add nWave-ai/nWave --skill nw-diverge

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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.

SKILL.md

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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:

# 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.

Gives 0 of the 12 instructions most research analysis skills give in ~1.9k tokens

Counted across 1,063 of the 1,754 authors here whose files we hold, read 2026-08-06

  • generate a markdown reportin 32 of 1063, across 17 files
  • cite each claim's sourcein 31 of 1063, across 14 files
  • define the ideal customer profilein 20 of 1063, across 2 files
  • search for companies matching the criteriain 20 of 1063, across 2 files
  • assign a fit score from one to tenin 20 of 1063, across 2 files
  • format results in a scannable markdown templatein 20 of 1063, across 2 files
  • analyze the codebase to understand the productin 19 of 1063, across 1 file
  • ask clarifying questions about the value propositionin 19 of 1063, across 1 file
  • look for signals of immediate needin 19 of 1063, across 1 file
  • identify the target decision maker rolein 19 of 1063, across 1 file
  • suggest a personalized contact strategyin 19 of 1063, across 1 file
  • provide conversation starters for outreachin 19 of 1063, across 1 file

Said here and by no other author read

  • Read prior wave artifacts before starting work
  • Stop if missing required product documentation
  • Load the skill file before starting work
  • Create tasks using a task tool
  • Extract and elevate the job from raw requests
  • Produce at least three outcome statements

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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