decision-matrix

name	decision-matrix
description	Structure a choice between concrete options: elicit the real criteria and their weights, score each option honestly, run a sensitivity check on the weights, and separate what the numbers say from what the gut says. Use when choosing between 2–6 named alternatives (jobs, tools, vendors, apartments, strategies).
compatibility	Created for Zo Computer
metadata	{"author":"rob.zo.computer"}

Decision Matrix

Use this skill when the user faces a choice between concrete, named options and wants the tradeoffs made explicit. The matrix is not an oracle — its value is forcing criteria and weights into the open, then revealing whether the decision is robust or hinges on one contestable weight.

Purpose

Unstructured option comparison lets the mind double-count favorite criteria and quietly ignore inconvenient ones. A weighted matrix forces every criterion to be named, weighted once, and applied consistently. The most important output is often not the winning score but the sensitivity analysis — discovering that the choice flips on a single weight is the real insight. And when the user feels disappointed by the winner, that feeling is data.

Procedure

1. List the options (2–6). If more, cluster or pre-filter first.
2. Elicit criteria — what actually matters. Probe for hidden criteria: status, fear, reversibility, optionality, who else is affected. Aim for 4–8.
3. Weight criteria (must sum to 100). Force tradeoffs — no "everything is important."
4. Score each option per criterion (1–10), citing a reason per cell — no bare numbers.
5. Compute weighted totals.
6. Sensitivity check: which single weight change would flip the winner? Is the margin robust or fragile?
7. Gut check: tell the user the winner and watch the reaction. If the numbers say A but the gut sinks, the matrix is missing a criterion — find it and re-run.
8. State the recommendation with its condition: "A wins unless you actually weight X more than you claimed."

Output format

Use this concise structure unless the user asks otherwise:

## Options
...

## Criteria and weights
| Criterion | Weight | Why it matters |
|---|---|---|

## Scores
| Criterion (weight) | Option A | Option B | Option C |
|---|---|---|---|
| ... | 7 — reason | 4 — reason | 9 — reason |
| **Weighted total** | ... | ... | ... |

## Sensitivity
...

## Gut check
...

## Recommendation
...

Guardrails

• No bare scores — every cell gets a reason, or the matrix is numerology.
• Include reversibility/optionality as a candidate criterion; irreversible choices deserve extra weight scrutiny.
• If two options are within ~5% after sensitivity analysis, say the matrix cannot decide and name the tiebreaker question.
• The matrix ranks given options; if all options are bad, say so rather than crowning the least bad.

Canonical questions

• "Should I take the Google offer, the startup offer, or stay?"
• "Postgres vs. DynamoDB vs. SQLite for this service?"
• "Which of these 3 apartments should we rent?"
• "Pick a city for the new office: Austin, Denver, or Raleigh."
• "Which vendor should we go with for payroll?"

Poor fits: yes/no decisions with one option (use assumption-audit or inversion), choices with unlistable criteria ("who should I marry"), and questions where the real task is generating options, not ranking them.