Decision · decision-matrix

Decision Matrix

Compare options against weighted criteria so the tradeoffs are visible.

Best for

Vendor selection, roadmap choices, hiring decisions, and product bets.

Can generate

Weighted scorecard, criteria rationale, recommendation, and sensitivity notes.

Good input

Help me compare several options with a weighted decision matrix and recommend the strongest choice.

Demo Gallery

What this skill can generate

Each demo maps to a real paid deliverable: a Markdown report, Mermaid diagram, or PDF-ready file. Users can inspect examples before spending their 3 free generations.

Markdown report1 credits

SaaS roadmap tradeoff · Complete Markdown report

A founding team needs to choose one Q3 product bet.

Generate this format

Sample input

We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

Generated output includes

  • Input summary and classic case context
  • Framework analysis table
  • Conclusion, risks, and next actions
  • Ready for Notion, Docs, or internal wikis

Full Markdown demo

# Decision Matrix: Q3 Product Bet

## Input Summary
We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

## Classic Case Context
We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

## Skill Used
- Decision Matrix
- Compare options against weighted criteria so the tradeoffs are visible.
- Best for: Vendor selection, roadmap choices, hiring decisions, and product bets.
- Can generate: Weighted scorecard, criteria rationale, recommendation, and sensitivity notes.

## Situation Judgment
The team must choose one Q3 focus among AI onboarding, enterprise permissions, and mobile performance, with visible metric movement within six weeks.

## Executive Summary
Prioritize AI onboarding, keep enterprise permissions as a scoped sales-support track, and use mobile performance as a quality gate. AI onboarding has the clearest activation upside and the fastest learning loop.

## Framework Analysis
| Option | Activation/revenue impact 35% | Six-week visibility 25% | Engineering control 20% | Enterprise stability 20% | Weighted view |
| --- | --- | --- | --- | --- | --- |
| AI onboarding | 5 | 4 | 3 | 4 | 4.15 |
| Enterprise permissions | 4 | 3 | 3 | 5 | 3.75 |
| Mobile performance | 3 | 4 | 4 | 3 | 3.45 |

## Reusable Diagram
This is a Markdown-only output. Switch to diagram or PDF-ready output to generate Mermaid.

## Recommendation
Make AI onboarding the main bet, measured by activation and first-value completion. Scope enterprise permissions to the two highest-frequency gaps, and set mobile error rate and load time as release gates.

## Risks And Unknowns
- If enterprise renewals are in an urgent window, enterprise stability deserves a higher weight.
- AI onboarding gains may hide segment differences, so do not rely on blended activation only.
- If mobile performance already hurts the core path, it can erase experiment gains.

## Next Actions
1. Confirm weights with founders and sales within one day.
2. Pull 90 days of activation-funnel data and identify the largest onboarding drop-off.
3. Define a two-week experiment version and stopping condition.
Diagram + report2 credits

SaaS roadmap tradeoff · Mermaid diagram + report

A founding team needs to choose one Q3 product bet.

Generate this format

Sample input

We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

Generated output includes

  • Complete Markdown report
  • Classic Mermaid diagram source
  • Visual preview on page
  • Downloadable .mmd file

Full Markdown demo

# Decision Matrix: Q3 Product Bet

## Input Summary
We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

## Classic Case Context
We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

## Skill Used
- Decision Matrix
- Compare options against weighted criteria so the tradeoffs are visible.
- Best for: Vendor selection, roadmap choices, hiring decisions, and product bets.
- Can generate: Weighted scorecard, criteria rationale, recommendation, and sensitivity notes.

## Situation Judgment
The team must choose one Q3 focus among AI onboarding, enterprise permissions, and mobile performance, with visible metric movement within six weeks.

## Executive Summary
Prioritize AI onboarding, keep enterprise permissions as a scoped sales-support track, and use mobile performance as a quality gate. AI onboarding has the clearest activation upside and the fastest learning loop.

## Framework Analysis
| Option | Activation/revenue impact 35% | Six-week visibility 25% | Engineering control 20% | Enterprise stability 20% | Weighted view |
| --- | --- | --- | --- | --- | --- |
| AI onboarding | 5 | 4 | 3 | 4 | 4.15 |
| Enterprise permissions | 4 | 3 | 3 | 5 | 3.75 |
| Mobile performance | 3 | 4 | 4 | 3 | 3.45 |

## Reusable Diagram
```mermaid
quadrantChart
  title Q3 product bet
  x-axis Low effort --> High effort
  y-axis Low impact --> High impact
  quadrant-1 Strategic bets
  quadrant-2 Quick wins
  quadrant-3 Avoid
  quadrant-4 Costly bets
  AI onboarding: [0.58, 0.86]
  Enterprise permissions: [0.72, 0.76]
  Mobile performance: [0.46, 0.62]
```

## Recommendation
Make AI onboarding the main bet, measured by activation and first-value completion. Scope enterprise permissions to the two highest-frequency gaps, and set mobile error rate and load time as release gates.

## Risks And Unknowns
- If enterprise renewals are in an urgent window, enterprise stability deserves a higher weight.
- AI onboarding gains may hide segment differences, so do not rely on blended activation only.
- If mobile performance already hurts the core path, it can erase experiment gains.

## Next Actions
1. Confirm weights with founders and sales within one day.
2. Pull 90 days of activation-funnel data and identify the largest onboarding drop-off.
3. Define a two-week experiment version and stopping condition.

Mermaid demo

quadrantChart
  title Q3 product bet
  x-axis Low effort --> High effort
  y-axis Low impact --> High impact
  quadrant-1 Strategic bets
  quadrant-2 Quick wins
  quadrant-3 Avoid
  quadrant-4 Costly bets
  AI onboarding: [0.58, 0.86]
  Enterprise permissions: [0.72, 0.76]
  Mobile performance: [0.46, 0.62]
PDF-ready file3 credits

SaaS roadmap tradeoff · PDF-ready HTML file

A founding team needs to choose one Q3 product bet.

Generate this format

Sample input

We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

Generated output includes

  • Complete Markdown content
  • Diagram source
  • Printable HTML
  • Ready to save as PDF for clients or executives

Full Markdown demo

# Decision Matrix: Q3 Product Bet

## Input Summary
We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

## Classic Case Context
We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

## Skill Used
- Decision Matrix
- Compare options against weighted criteria so the tradeoffs are visible.
- Best for: Vendor selection, roadmap choices, hiring decisions, and product bets.
- Can generate: Weighted scorecard, criteria rationale, recommendation, and sensitivity notes.

## Situation Judgment
The team must choose one Q3 focus among AI onboarding, enterprise permissions, and mobile performance, with visible metric movement within six weeks.

## Executive Summary
Prioritize AI onboarding, keep enterprise permissions as a scoped sales-support track, and use mobile performance as a quality gate. AI onboarding has the clearest activation upside and the fastest learning loop.

## Framework Analysis
| Option | Activation/revenue impact 35% | Six-week visibility 25% | Engineering control 20% | Enterprise stability 20% | Weighted view |
| --- | --- | --- | --- | --- | --- |
| AI onboarding | 5 | 4 | 3 | 4 | 4.15 |
| Enterprise permissions | 4 | 3 | 3 | 5 | 3.75 |
| Mobile performance | 3 | 4 | 4 | 3 | 3.45 |

## Reusable Diagram
```mermaid
quadrantChart
  title Q3 product bet
  x-axis Low effort --> High effort
  y-axis Low impact --> High impact
  quadrant-1 Strategic bets
  quadrant-2 Quick wins
  quadrant-3 Avoid
  quadrant-4 Costly bets
  AI onboarding: [0.58, 0.86]
  Enterprise permissions: [0.72, 0.76]
  Mobile performance: [0.46, 0.62]
```

## Recommendation
Make AI onboarding the main bet, measured by activation and first-value completion. Scope enterprise permissions to the two highest-frequency gaps, and set mobile error rate and load time as release gates.

## Risks And Unknowns
- If enterprise renewals are in an urgent window, enterprise stability deserves a higher weight.
- AI onboarding gains may hide segment differences, so do not rely on blended activation only.
- If mobile performance already hurts the core path, it can erase experiment gains.

## Next Actions
1. Confirm weights with founders and sales within one day.
2. Pull 90 days of activation-funnel data and identify the largest onboarding drop-off.
3. Define a two-week experiment version and stopping condition.

Mermaid demo

quadrantChart
  title Q3 product bet
  x-axis Low effort --> High effort
  y-axis Low impact --> High impact
  quadrant-1 Strategic bets
  quadrant-2 Quick wins
  quadrant-3 Avoid
  quadrant-4 Costly bets
  AI onboarding: [0.58, 0.86]
  Enterprise permissions: [0.72, 0.76]
  Mobile performance: [0.46, 0.62]

PDF-ready HTML demo

<!doctype html>
<html>
<head>
  <meta charset="utf-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1" />
  <title>Decision Matrix: Q3 Product Bet</title>
  <style>
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    h1 { font-size: 34px; line-height: 1.1; margin: 0 0 18px; }
    h2 { font-size: 20px; margin-top: 28px; }
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    @media print { body { margin: 18px; background: #fff; } .sheet { max-width: none; border: 0; padding: 0; } }
  </style>
</head>
<body>
  <main class="sheet">
    <p class="meta">ThinkOps AI PDF-ready output</p>
    <h1>Decision Matrix: Q3 Product Bet</h1>
    <pre># Decision Matrix: Q3 Product Bet

## Input Summary
We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

## Classic Case Context
We can only commit to one Q3 product direction: AI onboarding, enterprise permissions, or mobile performance. Sales believes permissions will protect enterprise renewals, growth believes AI onboarding will lift activation, and engineering worries performance debt will slow future releases. Constraints: 8 engineers, visible metric movement within six weeks, and no damage to existing enterprise reliability. Use a decision matrix to recommend a path, weights, and sensitivity checks.

## Skill Used
- Decision Matrix
- Compare options against weighted criteria so the tradeoffs are visible.
- Best for: Vendor selection, roadmap choices, hiring decisions, and product bets.
- Can generate: Weighted scorecard, criteria rationale, recommendation, and sensitivity notes.

## Situation Judgment
The team must choose one Q3 focus among AI onboarding, enterprise permissions, and mobile performance, with visible metric movement within six weeks.

## Executive Summary
Prioritize AI onboarding, keep enterprise permissions as a scoped sales-support track, and use mobile performance as a quality gate. AI onboarding has the clearest activation upside and the fastest learning loop.

## Framework Analysis
| Option | Activation/revenue impact 35% | Six-week visibility 25% | Engineering control 20% | Enterprise stability 20% | Weighted view |
| --- | --- | --- | --- | --- | --- |
| AI onboarding | 5 | 4 | 3 | 4 | 4.15 |
| Enterprise permissions | 4 | 3 | 3 | 5 | 3.75 |
| Mobile performance | 3 | 4 | 4 | 3 | 3.45 |

## Reusable Diagram
```mermaid
quadrantChart
  title Q3 product bet
  x-axis Low effort --&gt; High effort
  y-axis Low impact --&gt; High impact
  quadrant-1 Strategic bets
  quadrant-2 Quick wins
  quadrant-3 Avoid
  quadrant-4 Costly bets
  AI onboarding: [0.58, 0.86]
  Enterprise permissions: [0.72, 0.76]
  Mobile performance: [0.46, 0.62]
```

## Recommendation
Make AI onboarding the main bet, measured by activation and first-value completion. Scope enterprise permissions to the two highest-frequency gaps, and set mobile error rate and load time as release gates.

## Risks And Unknowns
- If enterprise renewals are in an urgent window, enterprise stability deserves a higher weight.
- AI onboarding gains may hide segment differences, so do not rely on blended activation only.
- If mobile performance already hurts the core path, it can erase experiment gains.

## Next Actions
1. Confirm weights with founders and sales within one day.
2. Pull 90 days of activation-funnel data and identify the largest onboarding drop-off.
3. Define a two-week experiment version and stopping condition.
</pre>
    <h2>Mermaid diagram source</h2><pre>quadrantChart
  title Q3 product bet
  x-axis Low effort --&gt; High effort
  y-axis Low impact --&gt; High impact
  quadrant-1 Strategic bets
  quadrant-2 Quick wins
  quadrant-3 Avoid
  quadrant-4 Costly bets
  AI onboarding: [0.58, 0.86]
  Enterprise permissions: [0.72, 0.76]
  Mobile performance: [0.46, 0.62]</pre>
  </main>
</body>
</html>

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