recommendation-canvas

Name: recommendation-canvas
Availability: InStock
Author: tabooes

Evaluate AI product proposals using a structured canvas of outcomes, hypotheses, risks, and positioning.

1|Updated Mar 30, 2026

Search Tags:#ai #strategy #canvas #hypothesis #value #risk #product-management

Ready to run

npx skills add https://github.com/tabooes/pm_skills --skill recommendation-canvas-tabooes

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: recommendation-canvas
Source: https://github.com/tabooes/pm_skills/tree/main/skills/recommendation-canvas
Command: npx skills add https://github.com/tabooes/pm_skills --skill recommendation-canvas-tabooes

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Recommendation Canvas helps product teams articulate AI investment bets into a structured, defendable recommendation that aligns stakeholders and clarifies risk, value, and strategy.

Core Features & Use Cases

Structured components: business outcome, product outcome, problem statement, solution hypothesis, positioning, assumptions, PESTEL risks, value justification, and success metrics.
Lightweight discovery: define 2-3 quick experiments to validate bets before committing engineering resources.
Executive-ready narrative: generates a complete, defendable proposal for decision-makers.

Quick Start

Fill out the canvas with the problem, target user, hypothesis, and success metrics to begin an evidence-based AI investment proposal.

Frequently Asked Questions about recommendation-canvas

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I structure an AI product strategy recommendation for stakeholders?▼

Structure an AI product strategy recommendation by synthesizing business outcomes, hypotheses, PESTEL risks, and value justification into a defensible canvas. This generates an executive-ready narrative that aligns stakeholders and clarifies risk before committing engineering resources.

What is the best way to evaluate AI product bets before committing engineering resources?▼

Evaluating AI product bets requires defining 2-3 quick lightweight discovery experiments to validate solution hypotheses. This approach tests assumptions and success metrics early, ensuring evidence-based validation before building full-scale AI features.

How do I align stakeholders on AI investment proposals and go/no-go decisions?▼

Align stakeholders on AI investments by articulating problem statements, positioning, and value justification within a repeatable canvas. This produces a defendable proposal that clarifies strategy, risk, and product outcomes for go/no-go decisions.

Can I use a structured canvas for lightweight discovery and AI feature hypothesis testing?▼

A structured canvas supports lightweight discovery by capturing target users, problem statements, and solution hypotheses. You define 2-3 quick experiments to validate bets, testing assumptions and success metrics before committing to engineering resources.

What components should an executive-ready AI investment proposal include?▼

An executive-ready AI investment proposal must include business outcomes, product outcomes, positioning, PESTEL risks, assumptions, and success metrics. These components synthesize into a complete, defendable narrative for decision-makers evaluating strategy and discovery steps.

When should I not use a recommendation canvas for AI product strategy?▼

Avoid using a recommendation canvas when you lack defined problem statements, target users, or success metrics to populate the hypothesis. The canvas requires articulating outcomes, risks, and value justification to produce a defensible strategy.