Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation.
Install
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add deanpeters/Product-Manager-Skills --skill recommendation-canvas -a cursor -y
Download skills/recommendation-canvas/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does recommendation-canvas live?
The publisher keeps it at https://github.com/deanpeters/Product-Manager-Skills.
Which agents can use recommendation-canvas?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation.
name: recommendation-canvas
argument-hint: "[AI product idea]"
description: Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation.
intent: >-
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.
type: component
theme: validation-experiments
best_for:
"Deciding whether an AI product idea deserves real investment"
"Surfacing the risks and hypotheses behind an AI feature request"
"Comparing AI solution options on outcomes rather than novelty"
scenarios:
"Leadership wants an AI feature and I need to evaluate whether it's worth building"
"I have three AI solution options and need to compare them on outcomes and risk"
estimated_time: "30-45 min"
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:
Core Components:
Business Outcome: What's in it for the business?
Product Outcome: What's in it for the customer?
Problem Statement: Persona-centric problem framing
Solution Hypothesis: If/then hypothesis with experiments
Positioning Statement: Value prop and differentiation
Assumptions & Unknowns: What could invalidate this?
## Business Outcome
- [e.g., "Reduce by 25% the churn of existing customers using our existing product"]
Example:
"Increase by 15% the monthly recurring revenue from enterprise customers within 12 months"
Quality checks:
Measurable: Can you track this metric?
Time-bound: Within what timeframe?
Ambitious but realistic: Not "10x revenue in 1 month"
Product Outcome
What's in it for the customer? Use this format:
[Direction] [Metric] [Outcome] [Context from persona's POV] [Acceptance Criteria]
## Product Outcome
- [e.g., "Increase the speed of finding patients when I know the inclusion and exclusion criteria"]
Example:
"Reduce by 60% the time spent manually processing invoices for small business owners"
Quality checks:
Customer-centric: Written from user perspective ("I," not "we")
Outcome, not feature: "Reduce time spent" not "Use AI automation"
Step 3: Frame the Problem
Use the problem framing narrative from skills/problem-statement/SKILL.md:
## The Problem Statement
### Problem Statement Narrative
- [Persona description: 2-3 sentences telling the persona's story from their POV]
- [Example: "Sarah is a freelance designer managing 10 clients. She spends 8 hours/month manually tracking invoices and chasing late payments. By the time she follows up, some clients have already moved to other designers, costing her revenue and damaging relationships."]
Quality checks:
Empathetic: Does this sound like the user's voice?
Specific: Not "users want better tools" but "Sarah spends 8 hours/month..."
Validated: Based on real user research, not assumptions
Step 4: Define the Solution Hypothesis
Hypothesis Statement
Use the epic hypothesis format from skills/epic-hypothesis/SKILL.md:
## Solution Hypothesis
### Hypothesis Statement
**If we** [action or solution on behalf of target persona]
**for** [target persona]
**Then we will** [attain or achieve desirable outcome]
Example:
"If we provide AI-powered invoice reminders that auto-send at optimal times for freelance designers, then we will reduce time spent on payment follow-ups by 70%"
Tiny Acts of Discovery
Define lightweight experiments to validate the hypothesis:
### Tiny Acts of Discovery
**We will test our assumption by:**
- [Experiment 1: Prototype AI reminder system and test with 5 freelancers]
- [Experiment 2: A/B test manual vs. AI-timed reminders for 20 users]
- [Experiment 3: Survey users on perceived value after 2 weeks]
Quality checks:
Fast: Days/weeks, not months
Cheap: Prototypes, concierge tests, not full builds
Falsifiable: Could prove you wrong
Proof-of-Life
Define validation measures:
### Proof-of-Life
**We know our hypothesis is valid if within** [timeframe]
**we observe:**
- [Quantitative outcome: e.g., "80% of users send reminders via the AI system"]
- [Qualitative outcome: e.g., "8 out of 10 users report saving 5+ hours/month"]
Step 5: Define Positioning
Use the positioning statement format from skills/positioning-statement/SKILL.md:
## Positioning Statement
### Value Proposition
**For** [target customer/user persona]
**that need** [statement of underserved need]
[product name]
**is a** [product category]
**that** [statement of benefit, focusing on outcomes]
### Differentiation Statement
**Unlike** [primary competitor or competitive arena]
[product name]
**provides** [unique differentiation, focusing on outcomes]
Testable: Each assumption can be validated via experiments
Step 7: Identify PESTEL Risks
Risks to Investigate (High Priority)
## Issues/Risks to Investigate
- **Political:** [e.g., "Regulatory changes to AI-generated communications"]
- **Economic:** [e.g., "Economic downturn reduces willingness to pay for premium features"]
- **Social:** [e.g., "Users may perceive AI reminders as impersonal or pushy"]
- **Technological:** [e.g., "AI model accuracy may degrade over time without retraining"]
- **Environmental:** [e.g., "Energy costs of AI processing"]
- **Legal:** [e.g., "GDPR compliance for storing customer email patterns"]
Risks to Monitor (Lower Priority)
## Issues/Risks to Monitor
- **Political:** [e.g., "Potential AI regulation in EU markets"]
- **Economic:** [e.g., "Exchange rate fluctuations affecting international customers"]
- **Social:** [e.g., "Changing norms around automated communication"]
- **Technological:** [e.g., "Emerging AI competitors with better models"]
- **Environmental:** [e.g., "Carbon footprint concerns from stakeholders"]
- **Legal:** [e.g., "Future data privacy laws"]
Step 8: Justify the Value
## Value Justification
### Is this Valuable?
- [Absolutely yes / Yes with caveats / No with suggested alternatives / Absolutely NO!]
### Solution Justification
We think this is a valuable idea. Here's why:
1. **[Justification 1]** - [Description, e.g., "Addresses the #1 pain point for our target segment"]
2. **[Justification 2]** - [Description, e.g., "Differentiates us from competitors who only offer manual reminders"]
3. **[Justification 3]** - [Description, e.g., "Low technical risk—leverages existing AI infrastructure"]
Step 9: Define Success Metrics
Use SMART metrics (Specific, Measurable, Attainable, Relevant, Time-Bound):
## Success Metrics
1. **[Metric 1]** - [e.g., "80% of active users adopt AI reminders within 3 months"]
2. **[Metric 2]** - [e.g., "Average time spent on payment follow-ups decreases by 50% within 6 months"]
3. **[Metric 3]** - [e.g., "Net Promoter Score for invoicing feature increases from 6 to 8 within 6 months"]
Step 10: Define Next Steps
## What's Next
1. **[Next step 1]** - [e.g., "Run 2-week prototype test with 10 beta users"]
2. **[Next step 2]** - [e.g., "Build lightweight AI model for reminder timing optimization"]
3. **[Next step 3]** - [e.g., "Conduct legal review of GDPR implications"]
4. **[Next step 4]** - [e.g., "Present findings to exec team for go/no-go decision"]
5. **[Next step 5]** - [e.g., "If validated, add to Q2 roadmap"]
Examples
See examples/sample.md for a full recommendation canvas example.
Mini example excerpt:
### Business Outcome
- Increase by 20% MRR from freelance users within 12 months
### Solution Hypothesis
**If we** provide AI-powered invoice reminders
**for** freelance designers
**Then we will** reduce time spent on follow-ups by 70%
Download skills/recommendation-canvas/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does recommendation-canvas live?
The publisher keeps it at https://github.com/deanpeters/Product-Manager-Skills.
Which agents can use recommendation-canvas?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.