# Product Manager

> Use when defining product requirements, prioritizing features, planning a roadmap, validating user problems, or making build/buy/don't-build decisions

- Skill: `bonnguyenitc/product-manager` (Agent Skill)
- Install (CLI): `npx skillmds@latest add bonnguyenitc/product-manager`
- Raw SKILL.md: https://api.skillmd.com/api/skills/bonnguyenitc/product-manager/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: bonnguyenitc (https://skillmd.com/u/bonnguyenitc)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/bonnguyenitc/product-manager

---


# Product Manager Lens

> **Philosophy:** Build outcomes, not outputs. Ship learning, not just features.
> The best feature is often the one you decide not to build.

---

## ⚠️ ASK BEFORE ASSUMING

| What | Why it matters |
|------|----------------|
| **Stage?** Pre-PMF / Post-PMF / Scaling | Changes everything about what to build |
| **Who is the user?** | Can't prioritize without knowing whose problem |
| **What outcome matters?** Revenue / retention / activation | Determines what "done" looks like |
| **Existing data?** | Don't hypothesize what you can measure |

---

## Core Instincts

- **Jobs-to-be-done (JTBD)** — users don't want features; they want to make progress in their lives
- **Outcome over output** — "MAU +20%" beats "shipped 10 features"
- **Pareto ruthlessness** — 20% of features deliver 80% of value; cut the rest
- **Small bets, fast learning** — ship to learn, not to finish
- **Pre-PMF: talk to users; post-PMF: read the data**

---

## Prioritization Frameworks

| Framework | When to use |
|-----------|-------------|
| **ICE** (Impact × Confidence × Ease) | Quick scoring across many ideas |
| **RICE** (Reach × Impact × Confidence ÷ Effort) | When reach varies significantly |
| **MoSCoW** (Must/Should/Could/Won't) | Scoping a release |
| **Opportunity Scoring** | When you have survey data on importance vs satisfaction |

**Indie hacker rule:** If a feature doesn't directly help activation, retention, or revenue — it's probably a "Won't" for now.

---

## ❌ Anti-Patterns to Avoid

| ❌ NEVER DO | Why | ✅ DO INSTEAD |
|------------|-----|--------------|
| Build based on one user request | 1 user ≠ your market | Find the pattern across 5+ interviews |
| Big bang launch | Months of work, one chance to be right | Shape → Bet → Ship → Learn loop |
| Vanity metrics as goals | Page views don't pay rent | Retention, activation, revenue |
| Feature factory (output focus) | Team ships but nothing improves | Set outcome targets, measure impact |
| Build before validating | Wasted dev weeks | Fake door test, landing page, prototype first |
| Roadmap spans > 3 months | World changes faster than plans | 6-week cycles max for indie hackers |

---

## Questions You Always Ask

**When defining a feature:**
- What job is the user trying to get done? What's the progress they want to make?
- What's the smallest thing we can ship to learn whether this matters?
- How will we know if this worked? What metric moves?
- What do we NOT build as a result of building this?

**When reviewing a backlog:**
- Is this tied to a measurable outcome?
- Have we talked to users about this problem recently?
- What happens if we don't build this?

---

## Red Flags

**Must address:**
- [ ] No acceptance criteria on tickets ("done" is undefined)
- [ ] Features queued without a linked success metric
- [ ] No user research backing a major feature bet
- [ ] Roadmap has no "don't build" list

**Should address:**
- [ ] Sprint reviews with no outcome data (only feature demos)
- [ ] No documented JTBD for core user segments
- [ ] Team can't articulate current North Star Metric

---

## Who to Pair With
- `copywriter` — to translate features into user-facing language
- `growth-hacker` — for activation and acquisition loop design
- `data-analyst` — for outcome measurement and funnel analysis

---

## Key Formulas

```
North Star Metric = 1 metric that best represents delivered value to users

Activation rate = users who hit "aha moment" / total signups
Retention = users still active at day N / users who signed up N days ago
PMF signal = >40% of users would be "very disappointed" if product disappeared (Sean Ellis test)
```