prd-v05-risk-discovery-interview_skill

This skill helps surface and prioritize project risks during PRD v0.5 reviews by guiding with questions and actionable mitigations.
  • Python

17

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill mattgierhart/prd-driven-context-engineering --skill prd-v05-risk-discovery-interview

  • SKILL.md7.9 KB

Overview

This skill conducts a guided risk-discovery interview for PRD v0.5 Red Team Review, surfacing market, technical, adoption, resource, dependency, and timing risks. It leads the user through context-driven questions, captures RISK- entries with owner decisions and mitigations, and primes the product for Technical Stack Selection. The goal is actionable risk registers, not idea-killing.

How this skill works

The AI reviews prior context artifacts (feature lists, journeys, business rules, constraints) and asks adaptive questions across six risk categories. For each identified risk it records a RISK- entry with description, trigger, user-assessed impact/likelihood, early signals, response type, mitigation steps, owner, and linked IDs. Finally it force-ranks risks and highlights the top 3–5 to act on before moving to stack selection.

When to use it

  • During PRD v0.5 Red Team Review to stress-test assumptions and surface blockers
  • When asked to 'identify risks', 'what could go wrong?', or 'stress test the idea'
  • Before choosing a technical stack so constraints inform architecture decisions
  • When you need a prioritized, owner-assigned risk register for validation or launch planning
  • If you want to convert vague concerns into specific mitigations and review dates

Best practices

  • Treat it as an interview: ask, listen, then summarize; avoid long monologues
  • Explore all six categories; don’t stop after market or technical risks alone
  • Force-rank risks by Impact × Likelihood and keep top 3–5 as action items
  • Assign a clear owner and concrete mitigation for every RISK- entry
  • Limit the register to 10–15 focused risks and schedule review dates

Example use cases

  • Startup preparing an MVP launch wants prioritized risks and mitigation owners
  • Product manager validating feature assumptions before committing developer time
  • Engineering lead deciding whether to self-host or buy a managed service based on dependency risks
  • Growth team mapping adoption frictions and early signals to avoid churn during week one
  • Leadership running a pre-mortem to surface hidden constraints before design lock

FAQ

Aim for 10–15 focused risks and force-rank them; highlight the top 3–5 that need active mitigation.

Who decides impact and likelihood?

The user assigns Impact and Likelihood during the interview; the AI facilitates scoring and ranking but the team owns the assessment.

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