nw-discuss

name	nw-discuss
description	Conducts Jobs-to-be-Done analysis, UX journey design, and requirements gathering through interactive discovery. Use when starting feature analysis, defining user stories, or creating acceptance criteria.
user-invocable	true
argument-hint	[feature-name] - Optional: --phase=[jtbd|journey|requirements] --interactive=[high|moderate] --output-format=[md|yaml]

NW-DISCUSS: Jobs-to-be-Done Analysis, UX Journey Design, and Requirements Gathering

Wave: DISCUSS (wave 2 of 6) | Agent: Luna (nw-product-owner) | Command: /nw-discuss

Overview

Execute DISCUSS wave through Luna's integrated workflow: JTBD analysis|UX journey discovery|emotional arc design|shared artifact tracking|requirements gathering|user story creation|acceptance criteria definition. Luna uncovers jobs users accomplish, maps to journeys and requirements, handles complete lifecycle from user motivations through DoR-validated stories ready for DESIGN. Establishes ATDD foundation.

For greenfield projects (no src/ code, no docs/feature/ history), Luna proposes Walking Skeleton as Feature 0.

Output Tiers (per D2)

Provenance: feature lean-wave-documentation — D2 (schema-typed sections), D10 (one-line expansion descriptions), DDD-7 (DISCUSS pilot wave), D6 (install-time pedagogical prompt). Tier-1 [REF] sections (always emitted) + Tier-2 EXPANSION CATALOG items (lazy, on-demand) are the two output bands. Full contract: nWave/skills/nw-density-resolution-contract/SKILL.md.

Tier-1 [REF] — always emitted

Under ## Wave: DISCUSS / [REF] <Section> headings:

• Persona ID — one-line user identifier mapped to the journey
• JTBD one-liner — single-sentence Job-to-be-Done statement
• Locked decisions — D-numbered design decisions with verdicts
• User stories with elevator pitches — every story has Before/After/Decision-enabled triplet
• Acceptance criteria (ACs) — testable, embedded per story
• Definition of Done (DoD) — 9-item checklist
• Out-of-scope — explicit non-goals
• WS strategy — A/B/C/D per Mandate 5
• Driving ports — inbound surfaces (CLI, skill, HTTP)
• Pre-requisites — dependencies on prior waves or features

Tier-2 EXPANSION CATALOG — lazy, on-demand (per D10)

Rendered under ## Wave: DISCUSS / [WHY|HOW] <Section> only when requested via --expand <id> (DDD-2), the wave-end menu (expansion_prompt = "ask" or "ask-intelligent"), mode = "full" auto-expansion, or an ad-hoc user request mid-session.

Expansion ID	Tier label	One-line description
jtbd-narrative	[WHY]	Full JTBD analysis: job dimensions (functional/emotional/social), four forces, opportunity scores
persona-narrative	[WHY]	Extended persona: goals, frustrations, mental model, vocabulary glossary
alternatives-considered	[WHY]	Decision rationale: alternatives weighed and rejected per locked decision
migration-playbook	[HOW]	Step-by-step migration guide for users on a prior version
journey-deep-dive	[HOW]	Full UX journey: emotional arc, shared artifacts registry, error-path map
gherkin-scenarios	[HOW]	Generated Gherkin scenarios covering happy path and key error paths
reviewer-findings-trace	[WHY]	R1-R10 reviewer findings chain with verdicts and how each landed in D1-D10
expansion-catalog-rationale	[WHY]	Why this set of expansions, why these defaults, why D10 enforces one-line descriptions

Density resolution (per D12)

Call resolve_density(global_config) from scripts/shared/density_config.py after reading ~/.nwave/global-config.json (missing/malformed = empty dict). Returns mode ("lean" | "full") + expansion_prompt ("ask" | "ask-intelligent" | "always-skip" | "always-expand" | "smart") per the D12 cascade (resolver-internal, DDD-5 — do NOT replicate locally). DISCUSS hard default is lean+ask-intelligent per Decision 4 (2026-04-28). Branch on density.mode (lean = Tier-1 only; full = Tier-1 + all Tier-2) and at wave end on density.expansion_prompt. Full cascade detail, branch semantics, ad-hoc override workflow: nWave/skills/nw-density-resolution-contract/SKILL.md.

Trigger detection (ask-intelligent mode, per Decision 4)

DISCUSS-specific extension on top of the shared contract. When expansion_prompt = "ask-intelligent", evaluate ALL triggers below against the wave artifacts produced so far. Each trigger that fires contributes its suggested expansion to a scoped menu. If NO trigger fires, emit no menu — strict lean output.

Trigger	Detection criterion	Suggested expansion
AC ambiguity	≥2 user stories share an AC where reasonable readers could disagree on the outcome	gherkin-scenarios
Cross-context complexity	Feature touches ≥3 bounded contexts (per DDD glossary) OR ≥3 distinct technologies	alternatives-considered
Multi-stakeholder need	≥3 distinct personas referenced across the user stories	persona-narrative
Compliance / regulatory	ACs reference regulatory terms (GDPR, HIPAA, SOX, audit, retention, encryption, PII, data residency)	migration-playbook (data migration) OR journey-deep-dive (user-facing)
WS strategy = D	Walking Skeleton strategy is "Configurable" (env-switching)	alternatives-considered

Menu when 1+ trigger fires: Suggested expansions for this feature (triggered by: {trigger names}): - {id}: {description} ... Apply? [Y/n/all/none/custom]. Do NOT show the generic 8-item Tier-2 catalog in ask-intelligent mode — only triggered items. Ad-hoc override path ("expand ") still works for any catalog item. Telemetry: one event per scoped-menu choice; when NO trigger fires, one choice = "skip" event with expansion_id = "*" records the silent-lean opportunity.

Telemetry (per D4 + DDD-6)

Every expansion choice emits a DocumentationDensityEvent (dataclass at src/des/domain/telemetry/documentation_density_event.py) via event.to_audit_event() → JsonlAuditLogWriter().log_event(...). Schema fields per D4: feature_id, wave, expansion_id, choice, timestamp. For this wave the schema declares "wave": "DISCUSS". Use helper scripts/shared/telemetry.py:write_density_event(...) — do NOT write JSONL directly.

Wave-specific signal: feeds DDD-7 pilot success metric (4) — "downstream agent regression — DESIGN consumes lean DISCUSS feature-delta.md and produces no --expand invocation". ask-intelligent emission rules: one expand event per scoped-menu acceptance; one skip event for no-trigger silent-lean; one skip event for triggers fired but user declined. Full emission rules + per-mode patterns: nWave/skills/nw-density-resolution-contract/SKILL.md.

Interactive Decision Points

Decision 1: Feature Type

Question: What type of feature is this? Options:

1. User-facing -- UI/UX functionality visible to end users
2. Backend -- APIs, services, data processing
3. Infrastructure -- DevOps, CI/CD, tooling
4. Cross-cutting -- Spans multiple layers (auth, logging, etc.)
5. Other -- user provides custom input

Decision 2: Walking Skeleton

Question: Should we start with a walking skeleton? Options:

1. Yes -- recommended for greenfield projects
2. Depends -- brownfield; Luna evaluates existing structure first
3. No -- feature is isolated enough to skip

Decision 3: UX Research Depth

Question: Priority for UX research depth? Options:

1. Lightweight -- quick journey map, focus on happy path
2. Comprehensive -- full experience mapping with emotional arcs
3. Deep-dive -- extensive user research, multiple personas, edge cases

Decision 4: JTBD Analysis

Question: Include Jobs-to-be-Done analysis? Options:

1. Yes -- mandatory by default. Every user-facing story must trace to a job_id in docs/product/jobs.yaml. Stories without job traceability fail Definition of Ready.
2. No (infrastructure-only escape valve) -- only permitted when the feature is a pure internal change (e.g. rename internal module, refactor build script) with no user-visible behavior. Requires job_id: infrastructure-only AND a infrastructure_rationale field on every story explaining why no user job applies. Reviewer will reject this option for any feature that touches user-facing surfaces.

Default: 1 (Yes). Rationale: STANDING rule "Tech-surface vs value-outcome backlog anti-pattern" (2026-04-24) — epics with tech-surface children but no JTBD framing fail to converge on done-state. Default-on JTBD enforces value-outcome framing at PO level.

Prior Wave Consultation

Before beginning DISCUSS work, read SSOT and prior wave artifacts:

1. SSOT (if docs/product/ exists):
   • docs/product/journeys/{name}.yaml — existing journey to extend (if applicable)
   • docs/product/jobs.yaml — validated jobs and opportunity scores
   • docs/product/vision.md — product vision
2. Project context: docs/project-brief.md | docs/stakeholders.yaml
3. DISCOVER artifacts: Read docs/feature/{feature-id}/discover/ (if present)
4. DIVERGE artifacts: Read docs/feature/{feature-id}/diverge/recommendation.md and job-analysis.md (if present — job is already validated, do not re-run JTBD)

Migration gate: If docs/product/ does not exist but docs/feature/ has existing features, STOP. The project has old-model features that should be migrated to SSOT before new waves run. Guide the user to docs/guides/migrating-to-ssot-model/README.md and complete the migration first. If docs/product/ does not exist and no old features exist (greenfield), DISCUSS will bootstrap it.

DISCUSS follows DISCOVER and optionally DIVERGE — reading SSOT first ensures continuity with prior features, then prior wave artifacts ground requirements in evidence.

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 (✓ {file} for each read, ⊘ {file} (not found) for missing). Do NOT skip files that exist — skipping causes requirements disconnected from evidence.

After reading, check whether any DISCUSS decisions would contradict DISCOVER evidence. Flag contradictions and resolve with user before proceeding. Example: DISCOVER found "users don't want automation" but DISCUSS story assumes "automated workflow" — this must be resolved.

Document Update (Back-Propagation)

When DISCUSS decisions change assumptions established in DISCOVER:

1. Document change — Add a ## Changed Assumptions section at the end of the affected DISCUSS artifact. Gate: section exists in artifact.
2. Reference original — Quote the original DISCOVER document and the original assumption verbatim. Gate: source document and quote both present.
3. State new assumption — State the new assumption and rationale for the change. Gate: rationale is explicit.
4. Preserve DISCOVER — Do NOT modify DISCOVER documents directly. Gate: DISCOVER documents unchanged.

Agent Invocation

@nw-product-owner

IF Decision 4 = Yes (default): Execute *jtbd-analysis for {feature-id}, then *journey informed by JTBD artifacts, then *story-map, then *gather-requirements with outcome KPIs. Every user story must include a job_id field traceable to docs/product/jobs.yaml. IF Decision 4 = No (infrastructure-only escape valve): Execute *journey for {feature-id}, then *story-map, then *gather-requirements with outcome KPIs. Every story must use job_id: infrastructure-only AND include an infrastructure_rationale field. Reviewer rejects this branch for any user-facing feature.

Context files: see Prior Wave Consultation above + project context files.

Configuration:

• format: visual | yaml | gherkin | all (default: all)
• research_depth: {Decision 3} | interactive: high | output_format: markdown
• elicitation_depth: comprehensive | feature_type: {Decision 1}
• walking_skeleton: {Decision 2}
• output_directory: docs/feature/{feature-id}/discuss/

Workflow

At the start of execution, create these tasks using TaskCreate and follow them in order:

Phase 1: Jobs-to-be-Done Analysis (DEFAULT — when Decision 4 = Yes; SKIPPED only for infrastructure-only escape valve)

Grounds all subsequent artifacts in real user motivations. Mandatory unless Decision 4 = No (infrastructure-only); reviewer enforces job traceability as a hard-blocking DoR check.

1. Job Discovery — Ask user what users are trying to accomplish. Capture in job story format: "When [situation], I want to [motivation], so I can [outcome]." Gate: all primary jobs documented in job story format.
2. Job Dimensions — For each job, identify functional (practical task), emotional (desired feeling), and social (desired perception) dimensions. Gate: three dimensions documented per job.
3. Four Forces Analysis — For each primary job, document Push (current frustration), Pull (desired future), Anxiety (adoption concerns), Habit (current behavior must change). Extract forces from interview transcripts, support tickets, or analytics when available rather than relying solely on user description. Gate: all four forces documented per job.
4. Opportunity Scoring — Rank jobs by importance vs. satisfaction gap. High importance + low satisfaction = strongest opportunities. Produce scored table. Gate: scored table produced when multiple jobs exist.
5. JTBD-to-Story Bridge — Map each job story to the user stories and acceptance criteria it will feed in Phase 3. Gate: every user story traces to at least one job.

Artifact	Path
Job Stories	docs/feature/{feature-id}/discuss/jtbd-job-stories.md
Four Forces	docs/feature/{feature-id}/discuss/jtbd-four-forces.md
Opportunity Scores	docs/feature/{feature-id}/discuss/jtbd-opportunity-scores.md (when multiple jobs)

Phase 1.5: Scope Assessment (Elephant Carpaccio early gate)

Per Decision 3 (2026-04-28): scope assessment runs BEFORE journey visualization investment to detect oversized features early and save rework. The agent (nw-product-owner) runs this as workflow Phase 2 (between Discovery and Journey Visualization). Heuristics: oversized signals (any 2+) = >10 user stories | >3 bounded contexts or modules | walking skeleton requires >5 integration points | estimated effort >2 weeks | multiple independent user outcomes that could ship separately. If oversized: propose splitting into independent thin end-to-end slices, ask user to confirm split before continuing. If right-sized: note ## Scope Assessment: PASS in wave-decisions.md. Deeper Elephant Carpaccio slicing happens later in Phase 2.5 (User Story Mapping). Gate: scope assessed; right-sized OR user-approved split confirmed.

Phase 2: Journey Design

Luna runs deep discovery (mental model|emotional arc|shared artifacts|error paths) informed by JTBD, produces visual journey + YAML schema + Gherkin scenarios. Each journey maps to one or more identified jobs.

1. Mental Model Discovery — Uncover user mental model: what users believe about the system, their vocabulary, and assumptions. Gate: mental model documented with no vague steps.
2. Happy Path Definition — Define all steps start-to-goal with expected outputs at each step. Gate: complete happy path with explicit outputs per step.
3. Emotional Arc Design — Map emotional state at each step. Confidence must build progressively toward goal. Gate: emotional arc coherent with upward trajectory.
4. Shared Artifact Tracking — Identify every ${variable} or artifact passed between steps. Document single source of truth for each. Gate: every shared artifact has one documented source.
5. Error Path Mapping — Identify failure modes and recovery paths for critical steps. Gate: error paths documented for each high-risk step.
6. Gherkin Scenario Generation — Produce Gherkin scenarios covering happy path and key error paths. Gate: scenarios cover all journey steps.

Artifact	Path
Visual Journey	docs/feature/{feature-id}/discuss/journey-{name}-visual.md
Journey Schema	docs/feature/{feature-id}/discuss/journey-{name}.yaml
Gherkin Scenarios	docs/feature/{feature-id}/discuss/journey-{name}.feature
Artifact Registry	docs/feature/{feature-id}/discuss/shared-artifacts-registry.md

Phase 2.5: User Story Mapping

Luna loads user-story-mapping skill before this phase.

1. Load Skill — Load user-story-mapping skill. Gate: skill loaded.
2. Backbone — Map user activities (big steps) horizontally across the top of the story map. Gate: all major activities identified and ordered.
3. Walking Skeleton — Identify minimum slice that delivers end-to-end value. Gate: walking skeleton slice defined.
4. Elephant Carpaccio Slicing — Decompose stories into thin vertical slices, each shipping end-to-end in ≤1 day (≤6 hours of crafter dispatch), each with a named learning hypothesis. This supersedes the old "group into at least two releases" gate. The discipline and its rationale are documented below. Gate: every slice has (a) end-to-end value, (b) ≤1 day ship estimate, (c) a named learning hypothesis of the form "disproves X if it fails", (d) production data (not synthetic), (e) a dogfood moment within the same day, (f) explicit IN/OUT scope lists.
5. Slice Taste Tests — Apply the carpaccio taste tests to each slice before committing:
   • If a slice lists "ship 4+ new components" → it is NOT thin. Split further.
   • If every slice depends on a new abstraction → ship the abstraction FIRST as its own slice (or postpone it).
   • If no slice disproves any pre-commitment → the slicing is decoration, not discipline. Rethink.
   • If a slice uses only synthetic data → it proves plumbing, not value. Require a production-data acceptance criterion.
   • If 2+ slices are identical except for scale → merge them. Gate: all taste tests pass OR the failures are documented with a reason.
6. Slice Briefs — Produce one brief per slice at docs/feature/{feature-id}/slices/slice-NN-name.md with: goal (one sentence), IN scope, OUT scope, learning hypothesis (what this disproves if it fails, what it confirms if it succeeds), acceptance criteria, dependencies, effort estimate, reference class, pre-slice SPIKE if uncertainty is high. Each brief is ≤100 lines. Gate: brief exists for each slice listed in the story map.
7. Prioritization — Suggest slice execution order based on (a) learning leverage (highest-uncertainty slices first, so failures cost less), (b) dependency chain, (c) dogfood cadence. Gate: prioritization rationale documented per slice, NOT just per release bucket.

Artifact	Path
Story Map	docs/feature/{feature-id}/discuss/story-map.md
Prioritization	docs/feature/{feature-id}/discuss/prioritization.md
Slice Briefs	docs/feature/{feature-id}/slices/slice-NN-*.md (one per slice)

Phase 3: Requirements and User Stories

Luna crafts LeanUX stories informed by JTBD + journey artifacts. Every story traces to at least one job story. Validates against DoR, prepares handoff. Per-wave peer review is OPTIONAL — the mandatory review gate is consolidated at end of DISTILL where Eclipse + Architect + Forge + Sentinel run in parallel against the full feature-delta.md (all 4 waves visible). Invoke per-wave review explicitly via /nw-review only when uncertainty warrants early feedback (e.g., novel domain, contested DoR, vendor-neutrality risk).

1. Story Drafting — Craft user stories in LeanUX format. Each story MUST trace to at least one job_id referencing a job in docs/product/jobs.yaml (Phase 1 output when Decision 4 = Yes). Infrastructure-only escape valve (Decision 4 = No): every story uses job_id: infrastructure-only AND includes an infrastructure_rationale field documenting why no user job applies — reviewer rejects this for user-facing features. Gate: every story has a job traceability reference (real job_id OR infrastructure-only with rationale). 1b. Elevator Pitch Test (MANDATORY, per-story) — Every user story MUST contain an ### Elevator Pitch subsection immediately after the story narrative, with exactly these three lines:

### Elevator Pitch
Before: {one sentence — what the user cannot do today}
After: run `{exact command / endpoint / UI action}` → sees `{exact observable output}`
Decision enabled: {one sentence — what the user decides with that output}

Rules:

• The "After" line MUST reference a real user-invocable entry point (CLI subcommand, HTTP endpoint path, UI action name) — not a service function or internal API
• The "sees" portion MUST describe concrete observable output (stdout text, HTTP response body, screen element) — not internal state or "tests green"
• The "Decision enabled" line is the Job-to-be-Done connection: if the user cannot make any decision with the output, the story is infrastructure, not value — merge it into the story that DOES enable a decision
• If a story legitimately has no user-visible output (pure infra migration), it MUST be labelled @infrastructure and BLOCK the slice — a slice containing only @infrastructure stories cannot be released

Slice composition hard gate (per Decision 2): any slice that contains ONLY @infrastructure stories (zero user-visible value stories) is a structural failure. The reviewer (nw-product-owner-reviewer) will REJECT the story-map and set verdict to rejected_pending_revisions. The PO must either (a) merge the slice with an adjacent value-bearing slice, or (b) split the @infrastructure work to land BEFORE the slice as a precursor commit (not a separately-shipped slice). This is hard-blocking: structural failure, not nit.

Gate: every non-@infrastructure story has a complete Elevator Pitch. Every slice contains at least one user-visible value story (slice composition hard gate).

2. Acceptance Criteria — Embed testable acceptance criteria in each story. Gate: every AC is verifiable without ambiguity. AC MUST verify the Elevator Pitch's "After" command produces the "sees" output end-to-end.
3. Requirements Completeness — Calculate requirements completeness score. Gate: score > 0.95.
4. Outcome KPIs — Define measurable outcome KPIs with targets. Gate: each KPI has a numeric target and measurement method.
5. DoR Validation — Validate all 9 DoR items with evidence. Gate: DoR passed with evidence for all 9 items.
6. Peer Review (OPTIONAL — per-wave; mandatory at end of DISTILL) — Per-wave Eclipse review is opt-in. Invoke explicitly via /nw-review nw-product-owner-reviewer only if (a) DoR validation surfaced ambiguity, (b) JTBD assumptions are unverified, (c) vendor-neutrality risk in story ACs, or (d) user explicitly requests. Default: skip. The mandatory consolidated review covering DISCUSS+DESIGN+DEVOPS+DISTILL fires at end of DISTILL. Gate: optional unless triggered.
7. Handoff Preparation — Confirm handoff acceptance by nw-solution-architect (DESIGN wave). Gate: handoff accepted.

Artifact	Path
User Stories (includes requirements + embedded AC)	docs/feature/{feature-id}/discuss/user-stories.md
DoR Validation	docs/feature/{feature-id}/discuss/dor-validation.md
Outcome KPIs	docs/feature/{feature-id}/discuss/outcome-kpis.md

Success Criteria

1. • JTBD analysis complete: all jobs in job story format (mandatory unless infrastructure-only escape valve)
2. • Job dimensions identified: functional|emotional|social per job
3. • Four Forces mapped per job (push|pull|anxiety|habit)
4. • Opportunity scores produced (when multiple jobs)
5. • UX journey map with emotional arcs and shared artifacts
6. • Every journey maps to at least one job
7. • Discovery complete: user mental model understood, no vague steps
8. • Happy path defined: all steps start-to-goal with expected outputs
9. • Emotional arc coherent: confidence builds progressively
10. • Shared artifacts tracked: every ${variable} has single documented source
11. • Story map created with backbone, walking skeleton, and elephant carpaccio slices (≤1 day each, each with a named learning hypothesis, each with its own slice brief at docs/feature/{id}/slices/slice-NN-*.md, all carpaccio taste tests passed)
12. • Outcome KPIs defined with measurable targets
13. • Prioritization suggestions based on outcome impact
14. • Requirements completeness score > 0.95
15. • Every user story traces to at least one job story (or job_id: infrastructure-only with rationale)
16. • All acceptance criteria testable
17. • DoR passed: all 9 items validated with evidence
18. • Per-wave peer review (OPTIONAL — invoked only on trigger; mandatory consolidated review fires at end of DISTILL)
19. • Handoff accepted by nw-solution-architect (DESIGN wave)

Next Wave

Handoff To: nw-solution-architect (DESIGN wave) + nw-platform-architect (DEVOPS wave, KPIs only) Deliverables: User stories + story map + outcome KPIs + SSOT journey/jobs updates | JTBD artifacts (when selected)

DISCUSS hands off to BOTH DESIGN (full artifacts) and DEVOPS (outcome-kpis.md only). DEVOPS and DESIGN can proceed in parallel — DESIGN receives the complete artifact set while DEVOPS receives only the KPI file to drive observability and instrumentation design.

Wave Decisions Summary

Before completing DISCUSS, produce docs/feature/{feature-id}/discuss/wave-decisions.md:

# DISCUSS Decisions — {feature-id}

## Key Decisions
- [D1] {decision}: {rationale} (see: {source-file})

## Requirements Summary
- Primary jobs/user needs: {1-3 sentence summary}
- Walking skeleton scope: {if applicable}
- Feature type: {user-facing|backend|infrastructure|cross-cutting}

## Constraints Established
- {constraint from requirements analysis}

## Upstream Changes
- {any DISCOVER assumptions changed, with rationale}

This summary enables DESIGN to quickly assess DISCUSS outcomes. DESIGN reads this plus key artifacts (user-stories.md, story-map.md, outcome-kpis.md) rather than all DISCUSS files.

Outputs

Single narrative file: docs/feature/{feature-id}/feature-delta.md — all DISCUSS findings (Tier-1 [REF] sections + any rendered Tier-2 expansions) live here. User stories with embedded AC, story map, DoR validation, outcome KPIs, wave-decisions all become ## Wave: DISCUSS / [REF|WHY|HOW] <Section> headings.

Machine artifacts (declared, parseable by downstream waves):

• docs/feature/{feature-id}/slices/slice-NN-*.md — slice briefs (one per elephant-carpaccio slice; consumed by DELIVER for roadmap step decomposition)

SSOT updates (per Recommendation 3 / back-propagation contract):

• docs/product/jobs.yaml — add validated job stories (functional/emotional/social dimensions, four forces, opportunity score)
• docs/product/journeys/{name}.yaml — create or extend journey schema (refines DISCOVER seed)
• docs/product/personas/{name}.yaml — create or extend persona profile

Legacy multi-file outputs (user-stories.md, story-map.md, dor-validation.md, outcome-kpis.md, wave-decisions.md, journey-{name}-visual.md as separate files) are NOT produced — that content lives in feature-delta.md. Validator: scripts/validation/validate_feature_layout.py.

Examples

Example 1: User-facing feature with comprehensive UX research

/nw-discuss first-time-setup

Orchestrator asks Decision 1-3. User selects "User-facing", "No skeleton", "Comprehensive". Luna starts with JTBD analysis: discovers jobs like "When I first open the app, I want to feel productive immediately, so I can justify the purchase." Maps four forces for each job. Scores opportunities. Then runs journey discovery informed by JTBD, produces visual journey + YAML + Gherkin. Finally crafts stories where each traces to a job, validates DoR, and prepares handoff.

Example 2: JTBD-only invocation

/nw-discuss --phase=jtbd onboarding-flow

Runs only Luna's JTBD analysis phase (job discovery + dimensions + four forces + opportunity scoring). Produces JTBD artifacts without proceeding to journey design or requirements. Useful for early discovery when you need to understand user motivations before committing to UX design.

Example 3: Journey-only invocation

/nw-discuss --phase=journey release-nwave

Runs only Luna's journey design phases (discovery + visualization + coherence validation). Produces journey artifacts without proceeding to requirements crafting. Useful when JTBD is already done and journey design needs standalone iteration.

Example 4: Requirements-only invocation

/nw-discuss --phase=requirements new-plugin-system

Runs only Luna's requirements phases (gathering + crafting + DoR validation). Assumes JTBD and journey artifacts already exist or are not needed (e.g., backend feature).