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recommendation-canvas

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项目 README

来源文件:README.md

抓取于 2026年8月2日

Product Manager Skills

GitHub stars License: CC BY-NC-SA 4.0 PRs Welcome Version Claude Code Plugin Skills

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║   70 battle-tested skills + 6 command workflows                    ║
║   Claude Code • Cursor • Codex  • n8n • OpenClaw • and more ...    ║
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║   v0.83 • July 17, 2026 • CC BY-NC-SA 4.0                          ║
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70 battle-tested PM frameworks, ready for Claude, Codex, ChatGPT, and any agent that can read structured knowledge.


Why This Exists

Generic AI output is a PM's worst enemy. When you tell your agent "write a PRD" without shared context, you get a generic document that no stakeholder trusts and no engineer can act on.

This library gives both you and your AI agent the same professional foundation: the why behind each framework, the failure modes to avoid, and the judgment to apply them correctly. You stop repeating yourself. Your agent stops guessing. The work gets better.

The goal is dual — functional and pedagogic in equal measure. Skills equip agents to do PM work at a professional level, and they teach the human PM the reasoning behind each framework — so you can explain it, adapt it, and pass it on. Neither is a byproduct of the other.


What You Can Get Done

Navigate by what you're actually trying to accomplish:

Framing and strategy

  • problem-framing-canvas — MITRE's Look Inward / Look Outward / Reframe sequence; stops teams from solving the wrong problem
  • positioning-statement — Geoffrey Moore's template for defining who you serve, what you solve, and how you're different
  • product-strategy-session — full strategy arc: positioning → problem framing → solution exploration → roadmap (2-4 weeks)

Stakeholder alignment

  • stakeholder-identification — map every stakeholder before engaging anyone: broad brainstorm → allies/audiences/influencers → R/P/D marking → equity lens → narrow to priority targets
  • stakeholder-mapping — run two complementary grids (Power × Interest for engagement strategy; Impact × Power for whose voice to elevate) and compare to find the gaps
  • stakeholder-engagement-advisor — per-stakeholder engagement planning: diagnoses their profile and context, then delivers tailored message framing, medium, cadence, and a named next action

Customer discovery and research

Prioritization and roadmapping

  • prioritization-advisor — asks 3-5 questions about your context, then recommends RICE, ICE, Kano, or the right alternative
  • epic-breakdown-advisor — splits large epics using Richard Lawrence's 9 patterns
  • roadmap-planning — gather inputs → define epics → prioritize → sequence → communicate (1-2 weeks)

Writing PM deliverables

  • user-story — Mike Cohn format + Gherkin acceptance criteria, with anti-patterns
  • prd-development — structured PRD: problem → personas → solution → metrics → stories (2-4 days)
  • press-release — Amazon Working Backwards: clarify product vision before writing a line of spec

Validation and experimentation

  • pol-probe-advisor — recommends which prototype type to run based on your hypothesis and risk level
  • pol-probe — template for documenting lightweight validation experiments before building

Finance and growth

  • business-health-diagnostic — diagnoses SaaS health across growth, retention, efficiency, and capital using your real metrics
  • organic-growth-advisor — McKinsey Growth Pyramid triage: diagnoses whether your constraint is in new segments, geographies, channels, or products
  • feature-investment-advisor — build / don't build recommendation using revenue impact, cost, ROI, and strategic value

Market and competitive intelligence

Career and leadership transitions

AI product work


Get Started

Choose your setup:

I use...Get thisNotes
Claude Desktop or Claude Webpm-skills-starter-pack.zipUnzip, then upload the individual skill ZIPs to Claude Skills
Claude CodePlugin marketplaceclaude /plugin marketplace add deanpeters/Product-Manager-Skills
Codexpm-skills-codex.zipInstalls .agents/skills and AGENTS.md
Not surepm-skills-starter-pack.zipStart here

All downloads: GitHub Releases

Themed packs for Claude Desktop / Web

Each pack below is a ZIP of upload-ready skill ZIPs — unzip, then upload individuals to Claude Skills:

PackDownloadWhat's inside
Starterpm-skills-starter-pack.zipCore skills across all categories
Discovery02-discovery-pack.zipResearch, interviewing, synthesis
Strategy03-strategy-pack.zipPositioning, roadmapping, prioritization
Delivery04-delivery-pack.zipPRDs, stories, epics
AI PM05-ai-pm-pack.zipContext engineering, orchestration, readiness
Market Intel06-market-intel-pack.zipThe full Market Intelligence Suite: disciplines, investigation chain, frameworks, monitors
All skills99-all-skills-pack.zipAll 70 skills

Install guides


Try It First — Streamlit (beta)

Not ready to wire skills into your agent setup? Run the local playground first and kick the tires in your browser.

pip install -r app/requirements.txt
streamlit run app/main.py

What you can do:

  • Learn — browse setup and integration paths without leaving the app
  • Find My Skill — describe your situation in plain English and get recommended skills
  • Run Skills — run a skill with your own scenario once you know what you want

Multi-provider support: Anthropic, OpenAI, Ollama. API keys via environment variables only (no in-app key entry).

Docs: app/STREAMLIT_INTERFACE.md · app/.env.example

Feedback welcome via GitHub Issues or LinkedIn.


70 Skills, 3 Types

Skills are organized in three tiers that build on each other:

┌────────────────────────────────────────────────────────┐
│  WORKFLOW SKILLS (19)                                  │
│  Complete end-to-end PM processes (days to weeks)      │
│  Example: run a full discovery cycle or write a PRD    │
└────────────────────────────────────────────────────────┘
                       ↓ orchestrates
┌────────────────────────────────────────────────────────┐
│  INTERACTIVE SKILLS (27)                               │
│  Guided discovery — 3-5 questions, then recommendations│
│  Example: "Which prioritization framework fits here?"  │
└────────────────────────────────────────────────────────┘
                       ↓ uses
┌────────────────────────────────────────────────────────┐
│  COMPONENT SKILLS (24)                                 │
│  Templates for specific PM deliverables (30-90 min)    │
│  Example: write a user story with acceptance criteria  │
└────────────────────────────────────────────────────────┘

Interactive skills use an Adaptive Decision Ladder. Instead of dumping a framework at you, an interactive skill asks 3-5 targeted questions about your specific context, then offers numbered recommendations — each with a clear "use this when" rationale. You pick a path. The skill executes it and explains the why as it goes. If you want to just learn the framework without doing the work, you can ask that too — the skill coaches you either way. This is ABC — Always Be Coaching — in practice.

Full catalog: catalog/INDEX.md — all 70 skills with descriptions, or browse skills/ directly.


How a Skill File Works

Every SKILL.md follows the same structure:

SectionWhat it contains
Frontmattername, description, type, intent, best_for, scenarios
PurposeWhat this skill does and when to reach for it
InputWhat you can bring (with example invocations) — inline input is used, not re-asked, and arriving empty-handed is fine: the skill walks you through it
Key ConceptsFrameworks, definitions, anti-patterns — with vocabulary explained
ApplicationStep-by-step instructions an agent (or human) can follow
ExamplesReal-world cases showing both good and bad versions
Common PitfallsNamed failure modes with consequences and corrections
ReferencesRelated skills and external frameworks

The best_for frontmatter field lists 3-5 specific scenarios where the skill is most useful — helpful for quickly scanning whether a skill fits your situation.

Why no $ARGUMENTS templating? Other skill libraries use Claude Code's $ARGUMENTS substitution for input. We deliberately don't: it only expands in Claude Code (it renders as literal syntax in Claude Desktop/Web, Codex, and the Streamlit playground), and it teaches the human reader nothing. Instead, every skill has a plain-language ## Input section that works on every runtime — and makes clear you can show up with full context, partial context, or nothing at all and be guided through the rest. Full rationale in CONTRIBUTING.md.


Works With

Claude Code · Claude Desktop · Claude Web · OpenAI Codex · ChatGPT · Cursor · Windsurf · n8n · LangFlow · CrewAI · Gemini · any agent that reads structured markdown

See docs/Platform Guides for PMs.md for platform-specific setup.


Docs

DocumentPurpose
Using PM Skills 101Beginner-friendly orientation — setup without technical overload
Platform Guides for PMsTool-by-tool setup chooser for every supported platform
Using PM Skills with ClaudeClaude Code + GitHub ZIP upload for Claude Desktop/Web
Using PM Skills with CodexLocal workspace + GitHub-connected Codex on ChatGPT
Using PM Skills with ChatGPTGitHub app, Custom GPT Knowledge, and Project-based usage
Using PM Skills with Slash Commands 101Turn skills into reusable slash commands like /pm-story
Add-a-Skill Utility GuideEnd-to-end guide for generating and validating new skills
Market Intelligence Suite SummaryThe 14-skill competitive/market research suite: disciplines, chain, and which skill to run when
Building PM SkillsHow raw PM content gets distilled into agent-ready skills
START_HERE.md60-second onboarding for local repo users

What's New

v0.83 — July 17, 2026 · The Market Intelligence Suite

v0.82 — July 8, 2026

  • Added incoming-request-advisor (Interactive) — drop in a Slack ping, email, mandate, or escalation and get a structured breakdown that separates the literal ask from the real job-to-be-done, reads sender power and stake, and points you toward a reply. Ships with a copy/paste template so you can run it by hand too
  • New: a browsable download shelf at /dist — no terminal, no Releases tab. Read the plain-language README, scan the CATALOG, and download any skill or pack straight from the repo. Built for PMs who just want the skills
  • Library now at 70 skills

v0.81 — July 4, 2026

  • Every skill now has a required ## Input section: what to bring, what happens to context you supply up front (it's used, not re-asked), and reassurance that arriving empty-handed is fine — the guided flow covers the rest
  • Added argument-hint autocomplete for Claude Code users; deliberately no $ARGUMENTS templating — it breaks on every other runtime and teaches the reader nothing (why)
  • Validator now enforces the convention: skills fail without an Input section or with bare $ARGUMENTS in the body
  • Streamlit playground shows each skill's "What to bring (all optional)" before you start a session
  • Restored agent-orchestration-advisor (Interactive) — the multi-agent workflow design skill was referenced everywhere but only existed on an orphaned commit; recovered from git history and brought up to current standards

v0.80 — June 19, 2026

  • Added stakeholder-identification (Component) — comprehensive stakeholder brainstorm using allies/audiences/influencers, R/P/D marking, equity lens, and bias check; narrows to priority targets
  • Added stakeholder-mapping (Component) — two complementary grids (Power × Interest + Impact × Power); comparing outputs reveals who you're under-engaging relative to how much the product affects them
  • Added stakeholder-engagement-advisor (Interactive) — per-stakeholder engagement planning via Adaptive Decision Ladder: three questions on profile, power/impact, and context deliver tailored message framing, medium, cadence, and a named next action

All three adapted from the MITRE Innovation Toolkit via the companion repo MITRE ITK Skills — worth a bookmark if you work in discovery, facilitation, or cross-functional product strategy.

v0.79 — May 15, 2026

  • Added organic-growth-advisor — McKinsey Growth Pyramid triage for new segments, geographies, channels, or products
  • Added pm-skill-creator — interactive skill for designing repo-compliant skills via guided conversation
  • Fixed missing .claude-plugin/plugin.json that silently blocked Claude Code skill discovery
  • Added configurable input length guard (PM_MAX_INPUT) and path traversal protection to helper scripts

→ Full changelog


Contributing

Found a gap? Have a PM framework worth formalizing? The bar is pedagogic — skills must teach the why, not just the how.

See CONTRIBUTING.md for guidelines, or open an issue to start a conversation.


License

CC BY-NC-SA 4.0 — non-commercial use with share-alike.

Everything in this repository — every skill, template, and doc — is licensed CC BY-NC-SA 4.0. There is no mix of licenses here.

Some skills note in their Provenance sections that they were adapted from product-manager-prompts, Dean's earlier prompt library. That repo has the same author, so there is no license conflict: a license grants permissions to other people, and a copyright holder is free to adapt and relicense their own work. Those Provenance lines are lineage — a breadcrumb back to where an idea started — not a license dependency. No third-party MIT-licensed text is incorporated anywhere in this library.

In plain terms:

  • ✅ Use these skills in your day job — at a for-profit company, with your team, in your agents. That's what they're for.
  • ✅ Adapt and remix them — share what you build under this same license, with credit.
  • ✅ Teach with them — workshops, brown bags, mentoring, sending the ladder down.
  • ❌ Don't sell them — no repackaging the skills themselves into a paid product, course, or service without expressed written permission.
  • 🤔 Not sure your use qualifies? Open an issue and ask. If you're using these in the spirit they were built — to get better at the craft and help others do the same — the answer is almost certainly yes.

The companion prompt library, product-manager-prompts, carries the same CC BY-NC-SA 4.0 license as of its v2.3.0, with its own plain-language permissions (stricter on commercial use) — see its LICENSING.md, which governs that repo.


Questions

其他

中风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 可能需要外部 token、网络权限或第三方服务。
  • 未检测到高风险命令。
  • 扫描发现:1 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/deanpeters/Product-Manager-Skills.git
  3. 将 "skills/recommendation-canvas" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/deanpeters/Product-Manager-Skills.git
  3. 将 "skills/recommendation-canvas" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/deanpeters/Product-Manager-Skills.git
  3. 将 "skills/recommendation-canvas" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/deanpeters/Product-Manager-Skills.git
  3. 将 "skills/recommendation-canvas" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/deanpeters/Product-Manager-Skills.git
  3. 将 "skills/recommendation-canvas" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
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

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:

  1. Business Outcome: What's in it for the business?
  2. Product Outcome: What's in it for the customer?
  3. Problem Statement: Persona-centric problem framing
  4. Solution Hypothesis: If/then hypothesis with experiments
  5. Positioning Statement: Value prop and differentiation
  6. Assumptions & Unknowns: What could invalidate this?
  7. PESTEL Risks: Political, Economic, Social, Technological, Environmental, Legal
  8. Value Justification: Why this is worth doing
  9. Success Metrics: SMART metrics to measure impact
  10. What's Next: Strategic next steps

Why This Works

  • Outcome-driven: Forces clarity on business AND customer value
  • Hypothesis-centric: Treats solution as a bet to validate, not a commitment
  • Risk-explicit: Makes assumptions and risks visible upfront
  • Executive-friendly: Comprehensive but structured for C-level review
  • AI-appropriate: Especially useful for AI features with high uncertainty

Anti-Patterns (What This Is NOT)

  • Not a PRD: This is strategic framing, not detailed requirements
  • Not a business case (yet): It informs the business case but needs validation first
  • Not a feature list: Focus on outcomes, not capabilities

When to Use This

  • Proposing a new AI-powered product or feature
  • Pitching to execs or securing budget/sponsorship
  • Evaluating whether an AI solution is worth pursuing
  • Aligning cross-functional stakeholders (product, engineering, data science, business)
  • After completing initial discovery (you need context to fill this out)

When NOT to Use This

  • For trivial features (don't over-engineer small tweaks)
  • Before any discovery work (you need user research and problem validation first)
  • As a replacement for experimentation (canvas informs experiments, not vice versa)

Application

Use template.md for the full fill-in structure.

Step 1: Gather Context

Before filling out the canvas, ensure you have:

  • Problem understanding: User research, pain points (reference skills/problem-statement/SKILL.md)
  • Persona clarity: Who experiences the problem? (reference skills/proto-persona/SKILL.md)
  • Market context: Competitive landscape, category positioning
  • Business constraints: Budget, timelines, strategic priorities

If missing context: Run discovery work first. This canvas synthesizes insights—it doesn't create them.


Step 2: Define Outcomes

Business Outcome

What's in it for the business? Use this format:

  • [Direction] [Metric] [Outcome] [Context] [Acceptance Criteria]
## 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]

Step 6: Document Assumptions & Unknowns

## Assumptions & Unknowns
- **[Assumption 1]** - [Description, e.g., "We assume users will trust AI-generated reminders"]
- **[Assumption 2]** - [Description, e.g., "We assume payment timing optimization increases response rates"]
- **[Unknown 1]** - [Description, e.g., "We don't know if users prefer email or SMS reminders"]

Quality checks:

  • Explicit: Make hidden assumptions visible
  • 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
<!-- Write these to convince C-level executives -->
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%

Common Pitfalls

Pitfall 1: Vague Outcomes

Symptom: "Business outcome: increase revenue. Product outcome: improve UX."

Consequence: No measurability or accountability.

Fix: Use the outcome formula: [Direction] [Metric] [Outcome] [Context] [Acceptance Criteria]. Be specific.


Pitfall 2: Solution-First Thinking

Symptom: Problem statement is "We need AI-powered X"

Consequence: You've jumped to solution without validating the problem.

Fix: Frame problem from user perspective. Let the solution hypothesis emerge from validated pain points.


Pitfall 3: Skipping Tiny Acts of Discovery

Symptom: Hypothesis → straight to roadmap, no experiments

Consequence: High risk of building the wrong thing.

Fix: Define 2-3 lightweight experiments. Test before committing engineering resources.


Pitfall 4: Generic PESTEL Risks

Symptom: "Political: regulations might change"

Consequence: Risk analysis is theater, not actionable.

Fix: Be specific: "GDPR compliance for storing client email timing data requires legal review."


Pitfall 5: Weak Value Justification

Symptom: "This is valuable because customers will like it"

Consequence: Not convincing to execs.

Fix: Use data: "Addresses #1 pain point per user research. 20% churn reduction = $500k ARR. Low tech risk."


References

Related Skills

  • skills/problem-statement/SKILL.md — Informs the problem narrative
  • skills/epic-hypothesis/SKILL.md — Informs the solution hypothesis structure
  • skills/positioning-statement/SKILL.md — Informs positioning section
  • skills/proto-persona/SKILL.md — Defines target persona
  • skills/jobs-to-be-done/SKILL.md — Informs customer outcomes

External Frameworks

  • Osterwalder's Value Proposition Canvas — Influences problem/solution framing
  • PESTEL Analysis — Risk assessment framework
  • SMART Goals — Success metrics structure

Dean's Work

  • AI Recommendation Canvas Template (created for Productside "AI Innovation for Product Managers" class)

Provenance

  • Adapted from prompts/recommendation-canvas-template.md in the https://github.com/deanpeters/product-manager-prompts repo.

Skill type: Component Suggested filename: recommendation-canvas.md Suggested placement: /skills/components/ Dependencies: References skills/problem-statement/SKILL.md, skills/epic-hypothesis/SKILL.md, skills/positioning-statement/SKILL.md, skills/proto-persona/SKILL.md, skills/jobs-to-be-done/SKILL.md

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