Recommendation Canvas
Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning.
Install
npx skills add deanpeters/Product-Manager-Skills --skill recommendation-canvas -g -a claude-code -y- Repository
- deanpeters/Product-Manager-Skills7.8k
- Category
- Engineering
- Updated
- 20d ago
- License
- See repository
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation.
Purpose
Evaluate and propose AI product solutions using a structured canvas that assesses business outcomes, customer outcomes, problem framing, solution hypotheses, positioning, risks, and value justification. Use this to build a comprehensive, defensible recommendation for stakeholders and decision-makers, especially when proposing AI-powered features or products that carry higher uncertainty and risk.
This is not a feature spec, it's a strategic proposal that articulates why this AI solution is worth building, what assumptions need validating, and how you'll measure success.
Input
Works best with: The AI product or feature idea being evaluated. Also useful: Target customer, expected business outcome, known risks, and who the recommendation must convince.
Anything supplied with the invocation itself, text after the skill name, a pasted context dump, or an appended ARGUMENTS: line, counts as answers already given. Use it and skip whatever it covers; don't re-ask.
Arriving empty-handed? That works too. The skill asks for the idea and the decision-maker, then works through the canvas boxes.
Example invocation: Recommendation canvas: AI-suggested reorder quantities for warehouse managers, VP Ops wants a go/no-go next month.
Key Concepts
The Recommendation Canvas Framework
Created for Dean Peters' Productside "AI Innovation for Product Managers" class, the canvas synthesizes multiple PM frameworks into one strategic view:
