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Frame ai product value

Skill alexe-ev/product-plugins/ai-product/skills/frame-ai-product-value

Frame the value of an AI feature in terms of user outcomes and business impact, not model capabilities. Use this skill when a team needs to articulate why an AI feature is worth building and what success looks like.From its SKILL.md

Install
npx -y skills add alexe-ev/product-plugins --skill frame-ai-product-value

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SKILL.md

2.6 KB, 488 tokens by cl100k_base, as published. Nobody here has run it

Frame AI Product Value

Purpose

Help teams move from "we can do this with AI" to "this AI capability solves a real user problem and creates measurable value" — grounding AI feature decisions in outcomes rather than technology.

Skill type

Conceptual skill

Use this skill when

  • An AI feature idea needs to be evaluated before investment
  • The team is excited about AI capability but unclear on user value
  • Stakeholders need a business case for an AI initiative
  • An AI feature's value proposition needs to be articulated for users, sales, or leadership

Do not use this skill when

  • The AI capability itself needs to be assessed (use assess-model-capabilities)
  • A business case needs full financial modeling (use build-business-case)

Required inputs

  • AI capability or feature idea
  • Target user segment

Optional inputs

  • User research on the problem area
  • Comparable AI features in the market
  • Cost or complexity of building
  • Existing baseline (what users do today without AI)

Upstream context

Works best when:

  • Product problem is identified
  • User research exists

Downstream handoff

Output can feed:

  • assess-model-capabilities (value frame → capability requirements)
  • design-human-in-loop-workflow (value frame informs where human oversight is needed)
  • build-business-case (value frame → business case inputs)

Instructions

  1. Identify the user problem the AI capability addresses.
  2. Describe what users do today without the AI (current behavior / workaround).
  3. Articulate the outcome the AI delivers for users (not the mechanism).
  4. Identify the business value: how does the user outcome translate to business metrics?
  5. Identify the risks: where could AI create harm, false confidence, or user distrust?
  6. Define what success looks like: specific, measurable outcomes.

Output

Provide:

  • User problem statement
  • Current user behavior without AI
  • AI-delivered outcome (user-centric)
  • Business value translation
  • Risk identification
  • Success definition
  • Build / don't build recommendation with rationale

Risks / caveats

  • "AI can do X" is not a value statement — always connect to user problem and outcome
  • AI features that impress in demos but create friction in daily use are not valuable
  • Identify risks before building, not after — especially for high-stakes decisions

What ships with it: 4 files

7.4 KB alongside SKILL.md

examples/

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