🎯

making-product-decisions

🎯Skill

from flpbalada/my-opencode-config

What it does

Helps product teams systematically evaluate complex decisions by providing a structured framework for analyzing tradeoffs, aligning stakeholders, and documenting decision-making processes.

📦

Part of

flpbalada/my-opencode-config(40 items)

making-product-decisions

Installation

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Quick InstallInstall with npx

npx skills add flpbalada/my-opencode-config --skill making-product-decisions

Need more details? View full documentation on GitHub →

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AddedFeb 4, 2026

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Skill Details

SKILL.md

Overview

# Making Product Decisions - Structured Decision Framework

A meta-framework for making and documenting product decisions. Combines decision

science principles with practical product management needs to ensure better

decisions, stakeholder alignment, and organizational learning.

When to Use This Skill

Choosing between competing priorities or approaches
Making irreversible or high-stakes decisions
Aligning stakeholders with different perspectives
Documenting decisions for future reference
Evaluating past decisions for learning
Delegating decision-making authority

Core Concepts

Decision Types (Bezos Framework)

```

+------------------+------------------+

| Type 1 | Type 2 |

| (One-way door) | (Two-way door) |

+------------------+------------------+

| Irreversible | Reversible |

| High stakes | Lower stakes |

| Slow, careful | Fast, iterate |

| Senior decision | Delegate widely |

+------------------+------------------+

```

Decision Quality vs. Outcome

| | Good Outcome | Bad Outcome |

| ----------------- | ---------------- | ---------------- |

| Good Decision | Deserved success | Bad luck |

| Bad Decision | Good luck | Deserved failure |

Judge decisions by process quality, not just outcomes.

Data-Informed vs. Data-Driven

| Approach | When to Use |

| ----------------- | -------------------------------------------------- |

| Data-driven | Clear metrics, sufficient data, understood system |

| Data-informed | Incomplete data, novel situations, judgment needed |

| Intuition-led | Time pressure, expert domain, pattern matching |

Most product decisions should be data-informed, not purely data-driven.

Analysis Framework

Step 1: Frame the Decision

| Element | Question |

| ----------------- | ------------------------------ |

| What | What exactly are we deciding? |

| Why | Why does this decision matter? |

| Who | Who should be involved? |

| When | When must we decide by? |

| Reversibility | Type 1 or Type 2 door? |

Step 2: Generate Options

Always have at least 3 options:

Do nothing / status quo
Option A
Option B

Avoid binary framing - it limits thinking.

Step 3: Establish Criteria

| Criterion | Weight | Why It Matters |

| ------------- | ------ | -------------- |

| [Criterion 1] | [1-5] | [Explanation] |

| [Criterion 2] | [1-5] | [Explanation] |

| [Criterion 3] | [1-5] | [Explanation] |

Step 4: Evaluate Options

| ---------- | ----------- | ----------- | ----------- | ----- |

Step 5: Document and Decide

Record:

Decision made
Rationale
Dissenting views
Success criteria
Review date

Output Template

```markdown

Product Decision Record

Decision: [Clear statement of what was decided] Date: [Date] **Decision

maker: [Name] Status:** [Proposed/Approved/Implemented]

Context

Problem/Opportunity: [What prompted this decision]

Constraints: [Time, resources, dependencies]

Reversibility: [Type 1 / Type 2]

Options Considered

| ---------- | ----------- | ---- | ---- |

| Status quo | [Desc] | [+] | [-] |

| Option A | [Desc] | [+] | [-] |

| Option B | [Desc] | [+] | [-] |

Decision Criteria

| Criterion | Weight | Rationale |

| --------- | ------ | --------- |

| [C1] | [1-5] | [Why] |

| [C2] | [1-5] | [Why] |

Evaluation

| Option | [C1] | [C2] | Weighted Total |

| ------- | ----- | ----- | -------------- |

| [Opt 1] | [x/5] | [x/5] | [Score] |

| [Opt 2] | [x/5] | [x/5] | [Score] |

Decision

Chosen option: [Option name]

Rationale: [Why this option best meets criteria]

Dissenting views: [Captured disagreements and concerns]

Success Criteria

| ------ | ------- | ------- | ---------- |

Review

Review date: [Date] What we'll evaluate: [Criteria for success/failure]

```

Real-World Examples

Example 1: Build vs. Buy

Decision: Build custom analytics or use third-party tool?

| ------------------ | ------ | ----- | --- |

| Time to market | 5 | 2 | 5 |

| Customization | 3 | 5 | 2 |

| Long-term cost | 4 | 3 | 4 |

| Maintenance burden | 4 | 2 | 5 |

| Total | | 42 | 66 |

Decision: Buy, despite customization limitations.

Example 2: Feature Prioritization

Decision: Next quarter focus - mobile app or API improvements?

Applied decision criteria:

Revenue impact (weight: 5)
User retention (weight: 4)
Strategic positioning (weight: 3)
Engineering complexity (weight: 2)

Result: Mobile app scored higher on revenue and retention despite higher

complexity.

Best Practices

Do

Make decision criteria explicit before evaluating
Include "do nothing" as an option
Document dissenting opinions
Set review dates for major decisions
Separate decision quality from outcome

Avoid

Analysis paralysis on Type 2 decisions
HiPPO (Highest Paid Person's Opinion) decisions
Retroactive justification
Ignoring intuition entirely
Forgetting to review past decisions

Decision Speed Guidelines

| Type | Approach |

| ------------------- | --------------------------------------- |

| Type 1, high stakes | Take time, involve stakeholders |

| Type 2, reversible | Decide quickly, iterate |

| Unclear type | Default to faster, can always slow down |

Integration with Other Methods

| Method | Combined Use |

| ------------------- | ------------------------------- |

| Hypothesis Tree | Structure analysis of options |

| Jobs-to-be-Done | Ground criteria in user needs |

| Five Whys | Understand decision root causes |

Resources

[Thinking in Bets - Annie Duke](https://www.amazon.com/Thinking-Bets-Making-Smarter-Decisions/dp/0735216355)
[Decisive - Chip & Dan Heath](https://www.amazon.com/Decisive-Make-Better-Choices-Life/dp/0307956393)

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