Brainstorm

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
• User-question prompts mean to ask the user directly in Codex.
• Ignore Claude-specific mode-switch instructions when they appear.
• Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
• Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
• Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
• For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
• If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex uses static project-reference loading instead of runtime-injected project docs. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

• docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
• docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
• docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Missing/stale context route: If docs/project-config.json, the docs index, lessons.md, CLAUDE.md, AGENTS.md, or any task-required reference doc is missing or stale, auto-run $project-init or the narrow setup route ($project-config, $docs-init, $scan-all, $scan --target=<key>, $claude-md-init) before ordinary project-specific work. If Codex mirrors or AGENTS.md are missing/stale, ask the user to run $sync-codex; do not auto-run it.

Situation-based docs:

• Project structure/architecture/tech-stack/deployment/setup (any layer — backend, frontend, or infra): project-structure-reference.md
• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec authoring, docs/specs/ pathing, or TC format: feature-spec-reference.md, spec-system-reference.md, spec-principles.md
• Behavior/public-contract changes or spec-test-code sync: workflow-spec-test-code-cycle-reference.md plus the spec docs above
• Derived spec indexes/ERDs/reimplementation guides: spec-system-reference.md and source Feature Specs under docs/specs/
• Integration test implementation/review: integration-test-reference.md
• E2E test implementation/review: e2e-test-reference.md
• Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

[BLOCKING] Execute skill steps in declared order. NEVER skip, reorder, or merge steps without explicit user approval. [BLOCKING] Before each step or sub-skill call, update task tracking: set in_progress when step starts, set completed when step ends. [BLOCKING] Every completed/skipped step MUST include brief evidence or explicit skip reason. [BLOCKING] If Task tools are unavailable, create and maintain an equivalent step-by-step plan tracker with the same status transitions.

Quick Summary

Goal: Facilitate a structured PO/BA brainstorming session via the Double Diamond process (diverge to discover problems and opportunities, then converge to validate and prioritize) to deliver a scored, ranked shortlist of 3-5 candidate ideas — each carrying a problem + value hypothesis, an identified riskiest assumption, and the cheapest validation test designed — so the team commits to the right problem AND the right solution before building, never to a flat unvalidated idea list. The default flow converges to ONE opinionated recommendation; the EXCEPTION is Multi-Opportunity Discovery mode, which instead outputs a ranked 3–8-item RICE opportunity map for user multi-select (each selected item becomes a separate PBI downstream).

Summary:

• Main steps (Phases 0→7) — the read-this-if-nothing-else map: P0 Session Setup (ask the user directly to detect scenario + role + how-much-known) → P1 Problem Framing / Diamond-1 diverge (POV statement → 5 Whys/Fishbone → JTBD job stories → 5–10 HMW) → P2 Opportunity Framing / Diamond-1 converge (OST / Lean Canvas / Blue-Ocean ERRC / Value-Prop Canvas) → P3 Ideation / Diamond-2 diverge (SCAMPER → Crazy 8s → Brainwriting 6-3-5 → Impact Mapping → Analogy = 25–40 raw ideas, zero judgment) → P4 Evaluation / Diamond-2 converge (Dot Vote → RICE → Kano → 2×2 → MoSCoW = ranked 3–5 shortlist) → P5 Hypothesis Validation (Problem + Value card + Riskiest-Assumption Test + cheapest test + Build-Measure-Learn per top-3) → P6 Decision (ONE opinionated recommendation + trade-offs) → P7 Documentation & Handoff. Multi-Opportunity Discovery mode swaps P6 for a ranked 3–8-item RICE opportunity map.
• Run ask the user directly Phase 0 FIRST to detect scenario (Problem-Solving / New Product / Enhancement / Multi-Opportunity Discovery), role (PO / BA / Mixed), and how-much-is-known — each scenario routes a different technique sequence (see Scenario Cheat Sheets) — why: misclassifying here derails every downstream phase.
• Strictly separate diverge (Phases 1 & 3 — generate, "Yes, and…", zero judgment) from converge (Phases 2 & 4 — narrow, RICE/Kano/MoSCoW scoring); mixing the two modes is the Golden Rule violation that kills idea output.
• Never stop at a raw or flat idea list: every top-3 candidate MUST carry a problem + value hypothesis card, an identified riskiest assumption (RAT), and the single cheapest validation test designed before any build commitment.
• Close with an opinionated decision (Phase 6 — recommend ONE option with trade-offs, not a menu), every claim evidence-backed at >80% confidence, then offer handoff by asking the user directly to $idea, $refine, $plan, etc. — EXCEPT in Multi-Opportunity Discovery mode, where convergence RANKS the opportunity map (3–8 RICE-scored items) and hands off via multi-select to a per-opportunity PBI loop instead of picking ONE winner.

Four Scenarios:

Double Diamond (master meta-framework):

DIAMOND 1: Right Problem          DIAMOND 2: Right Solution
────────────────────────────      ──────────────────────────
Discover ──► Define               Develop ──► Deliver
(diverge)    (converge)           (diverge)   (converge)

Golden Rule: NEVER evaluate ideas while generating them. Diverge and converge are separate modes. Mixing them kills creative output.

Be skeptical. Apply critical thinking. Every idea needs a testable hypothesis. Confidence >80% required before recommending.

Answer this question:

$ARGUMENTS

Phase 0: Session Setup (MANDATORY)

Use ask the user directly to detect scenario, role, and constraints before any technique.

0.1 — Scenario Detection

Ask:

1. "What scenario are we in?"
   • Problem-solving — something is broken, users struggle, a metric is bad
   • New product — greenfield, no existing product in this space
   • Feature enhancement — existing product, add/improve/remove capability
   • Multi-opportunity discovery — a raw product vision / problem statement spanning MULTIPLE distinct opportunities that should each become a separate PBI (do NOT converge to one — produce a ranked RICE opportunity map for multi-select; see Multi-Opportunity Discovery Mode)
   • Mixed — multiple of the above

Mode routing: If the input is a broad vision/problem spanning several distinct opportunities (typically driven by workflow-idea-to-pbi's MULTI-OPPORTUNITY DISCOVERY MODE), select Multi-opportunity discovery — it changes Phase 6 convergence from "pick ONE" to "rank a 3–8-item RICE opportunity map for multi-select." All other scenarios keep the single-recommendation default.

2. "What is the primary role in this session?"

   • Product Owner — outcome-focused, business value, user outcomes
   • Business Analyst — requirements-focused, process analysis, stakeholder mapping
   • Both PO + BA — full discovery and requirements
   • Developer / Architect — technical feasibility brainstorm

3. "How much is already known?"

   • Raw seed — just an intuition or observation
   • Problem confirmed — we know the problem, need solutions
   • Solution direction known — need to evaluate and score options
   • Idea exists — need hypothesis validation only

0.2 — Context Loading

• If project codebase exists: read docs/specs/ to understand domain
• If greenfield: skip codebase reading; rely on user input and web research
• Load docs/project-reference/domain-entities-reference.md if entity context needed
• Use WebSearch for market/competitor context when scenario = New Product or Enhancement

Phase 1: Problem Framing — Diamond 1 Diverge

Goal: Fully understand problem space before jumping to solutions. #1 brainstorming failure: solving the wrong problem.

Time-box: 20–45 minutes of session time.

1.1 — Problem Statement (POV Format)

Formulate a crisp problem statement BEFORE any ideation:

[User/Persona] needs [need/job-to-be-done]
because [insight/root cause/context],
but [current barrier/friction/failure].

Example:

Operators need to quickly identify high-priority orders for action
because peak-season backlogs delay fulfillment,
but the current system shows raw order data with no ranking or comparison.

Use ask the user directly to validate:

• "Is this the core problem, or a symptom of a deeper problem?"
• "Who specifically experiences this? How often? What's the cost?"
• "What evidence do we have this problem actually exists?"

1.2 — Root Cause Analysis (for Problem-Solving scenario)

Apply one of:

5 Whys:

Problem: [stated problem]
Why 1: [first cause]
Why 2: [cause of cause 1]
Why 3: [cause of cause 2]
Why 4: [cause of cause 3]
Why 5: [root cause] ← Fix HERE, not at Why 1

Fishbone (Ishikawa) — for systemic problems: Spine = problem statement. Bones = 6 cause categories:

• People, Process, Technology, Data, Environment, Policy
• For each bone: ask "What in this category could cause the problem?"

1.3 — JTBD (Jobs-To-Be-Done) — for New Product & Enhancement

Replace user stories with job stories to expose real motivation:

User Story (what): As an operator, I want to see order totals, so that I can make decisions.

Job Story (why + context): When I'm clearing a peak-season backlog with limited time, I want to instantly see which orders need action without opening every record, so I can make fast, defensible decisions before the cutoff.

Job Story Formula:

When [triggering situation + context],
I want to [motivation / job to be done],
so I can [outcome / expected result].

Generate 3–5 job stories covering main user segments. Each story = one opportunity.

1.4 — HMW (How Might We) Reframing

Transform problem statements into ideation-ready questions:

Formula: "How might we [verb] [object] so that [desired outcome]?"

From the POV statement:

• "How might we help HR managers rank employees so that promotion decisions take minutes not days?"
• "How might we surface hidden top performers so that managers discover talent they'd otherwise miss?"
• "How might we reduce bias in performance scoring so that promotion feels fair to all employees?"

Rules:

• Each HMW covers ONE idea direction
• Generate 5–10 HMW questions per problem
• Too broad = "How might we improve HR?" (useless) → too narrow = "How might we add a sort button?" (skip ideation, just build it)
• Sweet spot: one-concept questions that invite multiple solutions

Output of Phase 1:

• Problem statement (POV format)
• Root cause (5 Whys or Fishbone) — Problem-Solving only
• 3–5 Job Stories
• 5–10 HMW questions

Phase 2: Opportunity Framing — Diamond 1 Converge

Goal: Narrow problem space to highest-opportunity focus areas before ideating solutions.

2.1 — Opportunity Solution Tree (OST) — for Enhancement

Teresa Torres' framework. Maps desired outcome → opportunities → solutions → experiments.

Desired Outcome (business metric)
├── Opportunity 1 (unmet user need / pain / want)
│   ├── Solution A
│   └── Solution B
├── Opportunity 2
│   ├── Solution C
│   └── Solution D
└── Opportunity 3 (deprioritized)

Step 1: State ONE desired outcome (lagging metric the team owns — e.g., "Increase manager satisfaction with review process from 3.2 to 4.0 CSAT") Step 2: Map ALL known opportunities (pains, needs, wants) from research/interviews Step 3: For each top opportunity, generate solution directions (not detailed solutions yet) Step 4: Pick 1–2 opportunities to develop further in Phase 3

2.2 — Lean Canvas — for New Product

One-page business model for greenfield ideas (Ash Maurya):

Fill one canvas per major target segment. Keep it to 20 min — speed is the point.

2.3 — Blue Ocean ERRC Grid — for Enhancement or New Product

Eliminate-Reduce-Raise-Create grid (Chan Kim & Mauborgne):

Rule: Every innovation should have at least ONE item in Create AND one in Eliminate. A product with only Raise entries is incremental — not differentiated.

2.4 — Value Proposition Canvas

Connects customer profile to product value:

Customer Profile:

• Jobs (functional, social, emotional)
• Pains (frustrations, obstacles, risks)
• Gains (benefits, desires, measures of success)

Value Map:

• Products & Services (what you offer)
• Pain Relievers (how you reduce pains)
• Gain Creators (how you produce gains)

Fit = where Pain Relievers match Pains + Gain Creators match Gains.

Output of Phase 2:

• OST with 2 selected opportunities (Enhancement)
• Lean Canvas (New Product)
• ERRC grid (New Product or Enhancement)
• Value Proposition fit assessment

Phase 3: Ideation — Diamond 2 Diverge

Goal: Generate maximum quantity of solution ideas without judgment. Quality comes Phase 4.

Critical rule: NO evaluation in this phase. Every idea is valid. "Yes, and..." not "Yes, but..."

3.1 — SCAMPER

Apply each lens to the problem/existing product to generate solution directions:

Generate at least 2 ideas per SCAMPER letter = minimum 14 ideas.

3.2 — Crazy 8s (Rapid Visual Ideation)

Time-box: 8 minutes. 8 ideas. No refinement.

Process:

1. Fold paper into 8 sections (or create 8 boxes mentally)
2. Sketch one idea concept per box — rough is fine
3. Timer forces quantity over perfection
4. Share and build on sketches

For AI-facilitated sessions:

• AI generates 8 distinct solution directions in 2 minutes
• User picks top 3 to explore deeper
• Each direction = 1 sentence + 1 key differentiator

3.3 — Brainwriting 6-3-5

For multi-stakeholder sessions (async-friendly):

• 6 participants, 3 ideas each, 5 rounds
• Each round: read previous ideas → add 3 new ideas OR build on existing
• Result: up to 108 ideas in 30 minutes (works async via shared doc)

For AI-facilitated sessions:

• AI plays all 6 roles across 3 rounds
• Generates ideas from: PO perspective, BA perspective, End User perspective, Dev perspective, Ops perspective, Business perspective

3.4 — Impact Mapping

Gojko Adzic's technique. Maps Goal → Actors → Impacts → Deliverables:

GOAL: [business outcome with measurable target]
├── ACTOR: Who can help/hinder?
│   ├── IMPACT: How should behavior change?
│   │   └── DELIVERABLE: What feature produces this impact?
│   └── IMPACT: What negative behavior to prevent?
│       └── DELIVERABLE: What reduces this risk?
└── ACTOR: ...

Key insight: Work backward from GOAL. If a deliverable doesn't trace to an actor behavior change, don't build it.

3.5 — Analogical Thinking

"How does [industry X] solve [similar problem Y]?"

Output of Phase 3:

• SCAMPER grid with 14+ ideas
• Crazy 8s — 8 solution directions
• Impact Map (top 2 goals)
• Analogy-inspired ideas (3–5)
• Total raw idea count: target 25–40 ideas

Phase 4: Evaluation & Convergence — Diamond 2 Converge

Goal: Reduce 25–40 raw ideas to a ranked shortlist of 3–5 candidates for hypothesis testing.

4.1 — Dot Voting (First Pass)

Before scoring, do a quick gut-check elimination:

• Each idea gets a ✅ (keep) / ❌ (drop) / 🔄 (merge with another)
• Merge near-identical ideas
• Drop ideas that violate hard constraints (budget, tech, legal)
• Target: reduce to 10–15 candidates

4.2 — RICE Scoring

Rank remaining candidates:

RICE Score = (Reach × Impact × Confidence) / Effort

Reach:      Users affected per quarter (100 / 500 / 1000 / 5000+)
Impact:     0.25 minimal | 0.5 low | 1 medium | 2 high | 3 massive
Confidence: 0.5 low (gut feel) | 0.8 medium (some data) | 1.0 high (validated)
Effort:     Story Points — 1 trivial | 3 small | 5 medium | 8 large | 13 very large

Score all 10–15 candidates. Sort descending. Top 5 = shortlist.

4.3 — Kano Model Classification

For each shortlisted idea, classify:

Strategy: Must-Be → Performance → Delighter. Never skip Must-Be items for Delighters.

4.4 — Effort × Impact 2×2

Quick visual triage:

HIGH IMPACT
    │  Quick Wins ★    │  Major Projects ⚙️
    │  (do first)      │  (schedule carefully)
────┼──────────────────┼────────────────────
    │  Fill-Ins 📋     │  Money Pits ⚠️
    │  (if time)       │  (avoid or cut)
LOW IMPACT
         LOW EFFORT         HIGH EFFORT

Plot each shortlisted idea. Quick Wins = default first picks unless Major Project has strategic necessity.

4.5 — MoSCoW for Release Scope

For each idea in the shortlist, assign release priority:

Output of Phase 4:

• Dot-voted shortlist (10–15 ideas)
• RICE-scored table (top 5 ranked)
• Kano classification for each shortlisted idea
• 2×2 matrix placement
• MoSCoW assignment per idea

Phase 5: Hypothesis Validation

Goal: Before committing to build, test riskiest assumptions. 42% of startups fail from no market need — validate before building.

5.1 — Problem Hypothesis

**We believe** [target users/persona]
**Experience** [specific problem]
**Because** [root cause]
**We'll know this is true when** [validation metric/observable evidence]

Example:

We believe Operators
Experience frustration identifying high-priority orders during peak backlogs
Because order data is fragmented across 3 systems with no unified ranking
We'll know this is true when 3+ operators confirm they spend >2hrs per shift on manual data aggregation

5.2 — Value Hypothesis

**We believe** [feature/solution]
**Will deliver** [specific value/outcome]
**To** [target users]
**We'll know we're right when** [measurable success metric]

5.3 — Riskiest Assumption Test (RAT)

Identify the ONE assumption whose failure kills the idea:

1. List all assumptions: user behavior, technical feasibility, market demand, business model
2. Score each: Probability of being wrong (0–1) × Impact if wrong (0–1)
3. Highest score = Riskiest Assumption
4. Design cheapest possible test to validate/invalidate it before full build:
   • User interview (2–3 days)
   • Landing page / fake door test (1 week)
   • Prototype click-through (3–5 days)
   • Concierge MVP (1–2 weeks)
   • Smoke test / pre-sell (2–4 weeks)

5.4 — Build-Measure-Learn Loop

For each top idea, define the loop:

BUILD: Minimum experiment to test the assumption (not a full product)
MEASURE: One metric that proves/disproves the hypothesis
LEARN: What decision do we make if metric is met / not met?
PIVOT: If hypothesis invalidated — which alternative from Phase 3 do we try next?

Output of Phase 5:

• Problem hypothesis card per top-3 idea
• Value hypothesis card per top-3 idea
• Riskiest Assumption identified per idea
• Cheapest test designed
• Build-Measure-Learn loop defined

Phase 6: Decision & Recommendations

Goal: Present a clear, opinionated recommendation with trade-offs. Not "here are all the options" — "here's what we recommend and why."

6.1 — Top 3 Options Table

Present final shortlist as a decision table:

6.2 — Recommendation Statement

RECOMMENDED: [Option Name]

Why: [1–2 sentences on RICE + Kano + strategic fit]
Risk: [Primary risk + mitigation]
First step: [Cheapest test to validate before full commitment]
Time to validation: [Days/weeks]

6.3 — Dependency & Sequencing Check

• Does Option A depend on any existing feature/data/service not yet built?
• Can experiments run in parallel?
• What's the critical path to first validated learning?

Multi-Opportunity Discovery Mode

Selected in Phase 0.1 when the input is a raw product vision / problem statement spanning MULTIPLE distinct opportunities. This is an ADDITIONAL mode, not a replacement — every other scenario keeps the single-recommendation default of Phase 6.

When to use: the user hands off a broad vision, problem statement, or "explore this whole area" brief where several distinct, independently-shippable opportunities are expected — and each opportunity should become its own PBI downstream. This is the mode workflow-idea-to-pbi's MULTI-OPPORTUNITY DISCOVERY MODE drives.

How convergence differs (the defining difference): the default flow converges to ONE opinionated recommendation (Phase 6). This mode does NOT. Instead, the SAME convergence techniques (RICE / Kano / 2×2 from Phase 4) are used to RANK and present a map of 3–8 distinct opportunities — NOT to pick a single winner. The user then multi-selects which opportunities to develop. Picking one winner here would discard the other PBIs the downstream workflow exists to produce.

Technique flow: run Phases 1–4 as normal (problem framing → opportunity framing → ideation → convergence), but in Phase 2 lean on JTBD / Opportunity Solution Tree to surface the FULL opportunity landscape (not a single focus area), and in Phase 4 use RICE / Kano / 2×2 to SCORE and RANK every distinct opportunity rather than collapse to a top recommendation.

Output contract (must match what workflow-idea-to-pbi consumes):

• An opportunity map of 3–8 distinct, RICE-scored opportunities, ranked descending by RICE.
• Documented in plans/{plan-dir}/brainstorm-opportunity-map.md.
• Each opportunity carries: a one-line problem/value framing, RICE components (Reach × Impact × Confidence / Effort) + RICE score, and (where known) a Kano class — so each can seed a downstream PBI.

# Opportunity Map: [Vision/Problem]

| Rank | Opportunity | Problem/Value (1 line) | Reach | Impact | Confidence | Effort | RICE | Kano      |
| ---- | ----------- | ---------------------- | ----- | ------ | ---------- | ------ | ---- | --------- |
| 1    | ...         | ...                    | 1000  | 2      | 0.8        | 5      | 320  | Delighter |
| 2    | ...         | ...                    | ...   | ...    | ...        | ...    | ...  | ...       |

Multi-select handoff: present the ranked map by asking the user directly with multiSelect: true — "Which opportunities should we develop into PBIs?". The selected opportunities feed the per-opportunity PBI loop in workflow-idea-to-pbi (each selected opportunity → idea → refine → review → story → challenge → DoR → mockup, then a final cross-PBI prioritize). Do NOT author PBIs, specs, or plans inside this skill — the discovery mode's deliverable is the scored, multi-selected opportunity map only.

Phase 7: Documentation & Handoff

Report Output

Use naming pattern from ## Naming section in injected context.

Create markdown summary report:

# Brainstorm Session Report: [Topic]

## Session Context

- Scenario: [Problem-Solving / New Product / Enhancement]
- Role: [PO / BA / Mixed]
- Date: [YYYY-MM-DD]
- Input: [Original question/problem]

## Problem Statement

[POV format]

## Root Cause Analysis

[5 Whys or Fishbone — if Problem-Solving]

## Job Stories

1. [Job Story 1]
2. [Job Story 2]
3. [Job Story 3]

## HMW Questions

1. How might we...
2. How might we...

## Opportunity Map

[OST or Lean Canvas — per scenario]

## Raw Ideas Generated

[Total count: XX ideas across SCAMPER / Crazy 8s / Impact Mapping]

## Scored Shortlist (RICE)

| Rank | Idea | RICE | Kano | Effort | Priority    |
| ---- | ---- | ---- | ---- | ------ | ----------- |
| 1    | ...  | ...  | ...  | ...    | Must Have   |
| 2    | ...  | ...  | ...  | ...    | Should Have |

## Hypothesis Cards

### Top Recommendation: [Option Name]

- Problem Hypothesis: ...
- Value Hypothesis: ...
- Riskiest Assumption: ...
- Cheapest Test: ...
- Success Metric: ...

## Decision

[Recommendation + rationale]

## Next Steps

- [ ] [First concrete action]
- [ ] [Validation test]
- [ ] [Stakeholder alignment needed]

Technique Quick Reference

Role-Specific Guidance

PO Mode (Outcome Focus)

• Lead with: desired business outcome → opportunities → experiments
• Use: OST, Impact Mapping, RICE, Build-Measure-Learn
• Ask: "What behavior change do we need to see in users?"
• Resist: jumping to features before validating the outcome

BA Mode (Requirements Focus)

• Lead with: stakeholder needs → process gaps → requirements
• Use: BABOK elicitation (interviews, workshops, document analysis), Fishbone, JTBD
• Ask: "What does the system need to do to enable that behavior?"
• Resist: over-specifying before the PO validates the opportunity

Mixed PO + BA Mode

• PO owns: problem statement, opportunity framing, prioritization, hypothesis
• BA owns: requirements elicitation, acceptance criteria, edge cases, process mapping
• Handoff point: after Phase 4 (scored shortlist) → BA writes acceptance criteria per idea

Collaboration Tools

• planner agent — research industry best practices for specific domain
• docs-manager agent — understand existing feature constraints and domain context
• WebSearch — market/competitor context for new product scenarios
• docs-seeker skill — latest documentation for external plugins/APIs
• visual analysis tooling skill — analyze visual mockups, screenshots, competitor UIs
• sequential-thinking skill — complex problem decomposition requiring structured causal chains
• web-research skill — deep market research for greenfield or competitive analysis

Scenario Cheat Sheets

Scenario A: Problem-Solving

1. POV Statement → 2. 5 Whys / Fishbone → 3. HMW Questions
→ 4. SCAMPER on current solution → 5. RICE scoring
→ 6. Problem Hypothesis + RAT → 7. Recommend + cheapest test

Scenario B: New Product

1. Job Stories (JTBD) → 2. Lean Canvas → 3. Blue Ocean ERRC
→ 4. HMW Questions → 5. Crazy 8s / Brainwriting
→ 6. Kano Classification → 7. Value Hypothesis + RAT → 8. MVP scope

Scenario C: Feature Enhancement

1. Job Stories (JTBD) → 2. Opportunity Solution Tree
→ 3. HMW Questions → 4. SCAMPER on existing feature
→ 5. Impact Mapping → 6. RICE scoring → 7. 2×2 matrix
→ 8. Value Hypothesis + RAT → 9. Recommend + next experiment

Scenario D: Multi-Opportunity Discovery

1. Job Stories (JTBD) → 2. Opportunity Solution Tree (FULL landscape, not one focus)
→ 3. HMW Questions → 4. SCAMPER → 5. RICE-score EVERY opportunity
→ 6. Rank into a 3–8-item opportunity map (do NOT pick ONE)
→ 7. Write plans/{plan-dir}/brainstorm-opportunity-map.md
→ 8. ask the user directly multiSelect → hand selected opportunities to the per-opportunity PBI loop

Key difference from A/B/C: converge to a RANKED MAP for multi-select, never a single recommendation. See Multi-Opportunity Discovery Mode.

Anti-Patterns to Avoid

Critical Constraints

• DO NOT implement solutions — brainstorm and advise only
• DO validate hypotheses before endorsing any approach
• DO prioritize long-term maintainability over short-term convenience
• DO consider both technical excellence and business pragmatism
• DO produce a scored, ranked shortlist — never just a flat idea list
• DO always design the cheapest validation test — RAT before full spec

Workflow Integration

After brainstorm session concludes, use ask the user directly to present next steps:

Multi-Opportunity Discovery handoff: when run in discovery mode, do NOT pick a single next step — instead present the ranked 3–8-item RICE opportunity map (written to plans/{plan-dir}/brainstorm-opportunity-map.md) by asking the user directly with multiSelect: true, then hand the selected opportunities to workflow-idea-to-pbi's per-opportunity PBI loop. workflow-idea-to-pbi consumes this opportunity map directly.

[IMPORTANT] Use task tracking to break ALL work into small tas

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