/prd-v05-risk-discovery-interview

name: prd-v05-risk-discovery-interview
description: Surface risks through guided questioning, helping users consider pivots, constraints, and prioritization during PRD v0.5 Red Team Review. Triggers on requests to identify risks, stress-test the idea, perform red team review, or when user asks "what could go wrong?", "identify risks", "red team", "risk assessment", "challenge assumptions", "stress test the idea". Consumes all prior IDs (CFD-, BR-, FEA-, PER-, UJ-, SCR-) as interview context. Outputs RISK- entries with owner decisions and mitigations. Feeds v0.5 Technical Stack Selection.
context: fork
allowed-tools:
  - Read
  - Write
  - Edit
  - Glob
  - Grep
  - WebSearch
  - WebFetch

Risk Discovery Interview

Position in workflow: v0.4 Screen Flow Definition → **v0.5 Risk Discovery Interview** → v0.5 Technical Stack Selection

This is an **interactive interview skill**. The AI asks questions, the user reflects and decides. The goal is to surface risks so the user can mitigate or accept them—not to kill ideas.

Consumes

This skill requires prior work from v0.1-v0.4:

• **CFD-\* all customer feedback entries** (from v0.1-v0.2) — User research foundation; reveals confidence tier in market assumptions
• **BR-\* all business rules** (from v0.2-v0.3) — Constraints on what can change (pricing, moat, product type constrain risk responses)
• **FEA-\* feature entries** (from v0.3) — Feature complexity and priorities signal technical risks
• **PER-\* persona entries** (from v0.4) — Persona distribution and behaviors reveal adoption risks and churn signals
• **UJ-\* journey entries** (from v0.4) — Journey complexity signals friction points; long journeys increase adoption risk
• **SCR-\* screen entries** (from v0.4) — Screen count and design complexity informs technical resource risk

This skill assumes v0.1-v0.4 work is complete and serves as context for interview discovery.

Produces

This skill creates/updates:

• **RISK-\* entries** (risk discovery, owner-assigned severity) — Identified risks with Impact/Likelihood scoring (raw score from 1-9), response type (Mitigate/Accept/Avoid/Transfer), specific mitigations, and owners
• **README Risk Scorecard** — Baseline risk profile aggregated by category (Market/User/Technical) with total scores and risk level assessment
• **Risk mitigation summary** — Top 3-5 risks requiring active mitigation before v0.6 architecture work

All RISK- entries are created through user decision during the interview; they reflect explicit owner choices on severity, not AI assumptions:

Example RISK- entry (user-scored):

RISK-001: Market — Competitor Feature Parity
Description: Competitor X launches report scheduling feature (our FEA-003 planned) within 60 days
Trigger: Competitor announces roadmap; sees our landing page
Impact: High (3) — User severity assessment based on competitive urgency
Likelihood: Medium (2) — User assessment of competitor execution speed
Raw Score: 6 (3 × 2)
Status: open
Effective Score: 6.0

Early Signal: Competitor job postings for feature area, beta announcement
Response: Mitigate
Mitigation: Accelerate FEA-003 launch by 30 days; add scheduling as P0 (links to FEA-003, KPI-002)
Owner: Product Lead
Linked IDs: FEA-003 (report scheduling), KPI-002 (activation rate), BR-042 (undercut positioning)
Review Date: Weekly during v0.6 (architecture phase)
Added: v0.5

Note: Confidence scores are NOT part of RISK- entries. Risks are binary facts (discovered or not); severity is user-decided. Confidence applies to CFD-/FEA-/KPI- entries, not risks.

Design Principles

1. **Interview, not inquisition** — Facilitate discovery, don't interrogate 2. **Inform, not kill** — Surface risks so user can mitigate, not abandon 3. **User owns decisions** — AI facilitates, user assigns severity and response 4. **Actionable outputs** — Every risk has a mitigation path or explicit "accept"

Risk Categories

Discovery Categories (Interview Prompts)

| Category | Focus Area | Example Questions | |----------|------------|-------------------| | **Market** | Competitors, timing, demand | "What if [competitor] launches this feature next month?" | | **Technical** | Complexity, unknowns, dependencies | "Which feature has the most technical uncertainty?" | | **Adoption** | User behavior, activation, retention | "What's the biggest friction point in onboarding?" | | **Resource** | Team, budget, time | "If you had to cut scope by 50%, what stays?" | | **Dependency** | External factors, integrations, partners | "What external factor could block launch?" | | **Timing** | Deadlines, market windows, seasonality | "Is there a deadline we must hit? Why?" |

Scoring Categories (README Scorecard)

Each RISK- entry maps to one of 3 scoring categories for the README Risk Scorecard:

| Scoring Category | Discovery Categories | Measures | |---|---|---| | **Market** | Market, Timing | Will anyone buy this? | | **User** | Adoption, Dependency | Will users succeed with this? | | **Technical** | Technical, Resource | Can we build and run this? |

Interview Flow

Phase 1: Context Review

Before asking questions, AI reviews:

• CFD- evidence from v0.1-v0.2
• FEA- features and their priorities
• UJ- journeys and their complexity
• BR- business rules and constraints

Phase 2: Guided Questions

Ask questions from each category, adapting based on product context:

**Market Risks:**

• "What happens if [competitor] launches something similar in 60 days?"
• "What market assumption are you least confident about?"
• "What would cause users to choose a competitor instead?"

**Technical Risks:**

• "Which feature has the most technical uncertainty?"
• "What technology choice are you least confident about?"
• "Is there anything you've never built before?"

**Adoption Risks:**

• "What's the biggest friction point in [UJ-001 onboarding journey]?"
• "What behavior change are you asking users to make?"
• "What would cause a user to churn in the first week?"

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