Recommendation Canvas
Draft a structured product recommendation canvas that ties user pain, business outcomes, and testable discovery acts before building a feature.
Overview
recommendation-canvas is an agent skill most often used in Validate (also Idea, Build) that produces a markdown recommendation canvas linking outcomes, problem narrative, hypothesis, and discovery experiments.
Install
npx skills add https://github.com/deanpeters/product-manager-skills --skill recommendation-canvasWhat is this skill?
- Full example canvas (SmartReminders) linking business outcome, product outcome, and persona problem narrative
- Solution hypothesis framed as If/we/for/then with measurable targets (e.g. 70% time reduction, 30% on-time payments)
- Tiny Acts of Discovery and Proof-of-Life windows (e.g. 4-week observation criteria)
- Qualitative and quantitative success signals for indie teams without a formal PMO
- Worked example targets 20% MRR lift and 70% reduction in follow-up time in the SmartReminders narrative
- Tiny discovery example includes 2-week prototype with 5 freelancers
Adoption & trust: 1.2k installs on skills.sh; 5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have a feature idea but no shared doc that connects user pain, business metrics, and what you will try in the next two weeks.
Who is it for?
Indie SaaS founders scoping one meaningful feature (billing, reminders, onboarding) before engineering starts.
Skip if: Engineering-only tasks with an approved spec, or enterprises that already mandate a different portfolio governance template.
When should I use this skill?
You are deciding whether to build a specific product recommendation and need outcomes, problem story, and experiments in one canvas.
What do I get? / Deliverables
You leave with a filled recommendation canvas and explicit tiny discovery acts so the next step is prototyping or killing the bet with criteria, not vibes.
- Markdown recommendation canvas with hypothesis and discovery plan
- Proof-of-Life observation criteria for a fixed time window
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Validate is where scope and evidence-backed bets are locked; the canvas is the canonical artifact before full Build commitment. Scoping a feature recommendation maps problem, outcomes, and tiny experiments—core validate/scope work for solo PM-founder hybrids.
Where it fits
Turn interview notes about late payments into a named bet with MRR-linked business outcome.
Define Tiny Acts of Discovery before committing two weeks to an AI reminder prototype.
Reconcile sprint scope against Proof-of-Life metrics agreed in the canvas.
How it compares
Use instead of unstructured PRDs when you need outcome-linked hypotheses and proof-of-life gates for a single recommendation.
Common Questions / FAQ
Who is recommendation-canvas for?
Solo and indie product builders who need a lightweight PM canvas without a dedicated product team.
When should I use recommendation-canvas?
In Idea when prioritizing opportunities, in Validate when scoping a feature bet, and in Build/pm when aligning a sprint goal to business outcomes.
Is recommendation-canvas safe to install?
It outputs planning markdown only; review the Security Audits panel on this catalog page for the skill package source and permissions.
SKILL.md
READMESKILL.md - Recommendation Canvas
# Recommendation Canvas Example ## Example: AI-Powered Invoice Reminder System (Good Canvas) ```markdown ## AI Invoice Reminder Canvas ### Product Name - SmartReminders ### Business Outcome - Increase by 20% the monthly recurring revenue from freelance users within 12 months by reducing churn caused by payment frustration ### Product Outcome - Reduce by 70% the time freelancers spend chasing late payments when using our invoicing platform ### The Problem Statement Sarah is a freelance designer managing 10 clients at once. She spends 5+ hours per month manually tracking overdue invoices and sending follow-up emails. By the time she follows up, some clients have already forgotten or moved on, costing her revenue and damaging relationships. She feels anxious and frustrated, wishing she could focus on design instead of admin. ### Solution Hypothesis **If we** provide AI-powered invoice reminders that auto-send at optimal times **for** freelance designers using our invoicing platform **Then we will** reduce time spent on payment follow-ups by 70% and increase on-time payment rates by 30% **Tiny Acts of Discovery:** - Prototype AI reminder system and test with 5 freelancers for 2 weeks - A/B test manual vs. AI-timed reminders with 20 users - Survey users on perceived value and trust in AI-generated messages **Proof-of-Life:** Within 4 weeks, we observe: - 80% of test users adopt AI reminders (quantitative) - 8 out of 10 users report saving 3+ hours/month (qualitative) - On-time payment rate increases by 20% (quantitative) ### Positioning Statement **For** freelance creative professionals **that need** to reduce time spent chasing late payments without damaging client relationships SmartReminders **is an** AI-powered invoicing automation feature **that** automatically sends payment reminders at optimal times, increasing on-time payments by 30% **Unlike** manual reminder systems or generic email schedulers SmartReminders **provides** AI-optimized timing based on client behavior patterns, maximizing response rates without appearing pushy ### Assumptions & Unknowns - **Assumption 1:** Users will trust AI to send messages on their behalf - **Assumption 2:** Optimal reminder timing improves payment rates - **Unknown 1:** Whether users prefer email, SMS, or in-app reminders - **Unknown 2:** How to handle clients who never respond to reminders ### Issues/Risks to Investigate - **Political:** N/A (low risk) - **Economic:** Recession may reduce willingness to pay for premium features - **Social:** Users may perceive AI reminders as impersonal or spammy - **Technological:** AI model accuracy may degrade without retraining - **Environmental:** Minimal (low energy costs) - **Legal:** GDPR compliance for storing client email patterns and timing data ### Issues/Risks to Monitor - **Political:** Future AI regulation - **Economic:** Currency fluctuations for international users - **Social:** Changing norms around automated business communication - **Technological:** Competitor AI models - **Environmental:** Carbon footprint concerns - **Legal:** Evolving data privacy laws ### Value Justification **Is this Valuable?** - Absolutely yes **Solution Justification:** 1. **Addresses top pain point:** Payment follow-ups are the #1 complaint from freelance users (per user research) 2. **Differentiates from competitors:** Competitors offer manual reminders; we offer AI-optimized timing 3. **Low technical risk:** Leverages existing AI infrastructure and user behavior data 4. **High business impact:** Reducing churn by 20% = $500k ARR ### Success Metrics 1. **Adoption:** 75% of active invoicing users enable AI reminders within 3 months 2. **Time savings:** Average time spent on payment follow-ups decreases by 60% within 6 months 3. **Payment rates:** On-time payment rate increases from 50% to 65% within 6 months 4. **NPS:** Net Promoter Score for invoicing feature increases from 6 to 8 ### What's Next 1. **Run prototype test:** 2-week test