decision-matrix
GitHub结构化决策辅助工具，运用利弊分析、加权矩阵、事前验尸及机会成本等框架，帮助用户量化评估选项，降低决策不确定性，得出清晰可辩护的结论。
Trigger Scenarios
Install
npx skills add cosmicstack-labs/mercury-agent-skills --skill decision-matrix -g -y
SKILL.md
Frontmatter
{
"name": "decision-matrix",
"metadata": {
"tags": [
"decision-making",
"frameworks",
"productivity",
"strategy",
"prioritization"
],
"author": "cosmicstack-labs",
"version": "1.0.0",
"category": "creative-personal-development"
},
"description": "A structured reasoning assistant that helps you make clear, confident decisions using proven frameworks — Pros\/Cons, Decision Matrix (Pugh Matrix), Weighted Scoring, Pre-mortem, and Opportunity Cost Analysis."
}
Decision Matrix
What It Does
Takes the guesswork out of tough decisions by applying structured frameworks. Instead of spinning in circles with pros/cons lists, you get a repeatable system for comparing options, weighting what matters, and reaching a conclusion you can explain and defend.
Frameworks Available
1. Classic Pros & Cons (Benjamin Franklin Method)
Best for: Quick decisions with low-to-moderate stakes
| Step | Action |
|---|---|
| 1 | Draw two columns: PROS and CONS |
| 2 | List every reason for and against — no filtering |
| 3 | Weigh each item (not all pros are equal). Assign +1 to +5 for pros, -1 to -5 for cons |
| 4 | Sum the scores. A net positive suggests "yes." Consider emotional weight too |
Guardrail: Pros/cons alone miss hidden assumptions. Always follow with: "What am I not considering?"
2. Weighted Decision Matrix (Pugh Matrix)
Best for: Comparing multiple options against multiple criteria
| Criteria | Weight (1-5) | Option A | Option B | Option C |
|------------------------|-------------|----------|----------|----------|
| Cost | 4 | 8/10 | 6/10 | 9/10 |
| Time to Market | 3 | 7/10 | 9/10 | 5/10 |
| Strategic Fit | 5 | 9/10 | 4/10 | 7/10 |
| Team Capacity | 2 | 6/10 | 8/10 | 4/10 |
| **Weighted Total** | | 113 | 92 | 103 |
Steps:
- List all viable options (rows)
- Define criteria that matter (columns)
- Assign a weight (1-5) to each criterion based on importance
- Score each option per criterion (1-10)
- Multiply score × weight, sum across criteria
- Highest weighted total wins — but sanity-check the result
3. Pre-Mortem
Best for: High-stakes decisions where risk mitigation is critical
"It's 12 months from now and our decision has failed spectacularly. How did it happen?"
| Step | Technique |
|---|---|
| 1 | Assume the decision was made and led to disaster |
| 2 | Fast-forward and write the "post-mortem" — what went wrong? |
| 3 | Generate 5-10 plausible failure modes |
| 4 | For each failure, ask: "What could prevent this?" |
| 5 | Incorporate those safeguards into the decision |
Why it works: Humans are loss-averse. Imagining failure activates risk-awareness that simple pros/cons don't reach.
4. Opportunity Cost Frame
Best for: Deciding between two good options (where saying yes to A means saying no to B)
| Frame | Question |
|---|---|
| Cost of yes | What do I give up by choosing this? |
| Cost of no | What do I give up by not choosing this? |
| Regret test | If I look back in 5 years, which "no" would I regret more? |
| Opportunity comparison | If Option A didn't exist, would I choose Option B? |
Heuristic: If saying "no" to Option A doesn't feel like a loss, don't choose it.
5. ICE Score (Impact, Confidence, Ease)
Best for: Prioritizing many options quickly (features, ideas, experiments)
| Criterion | Scale | Question |
|---|---|---|
| Impact | 1-10 | How significant will the result be if successful? |
| Confidence | 1-10 | How sure are we about the expected outcome? |
| Ease | 1-10 | How easy/simple is this to execute? |
Formula: ICE Score = Impact × Confidence × Ease
Sort by score. Work on the highest first. Re-score when new data emerges.
6. The 10/10/10 Rule
Best for: Emotional or high-stakes personal decisions
| Time Horizon | Question |
|---|---|
| 10 minutes | How will I feel about this decision in 10 minutes? |
| 10 months | How will I feel about it in 10 months? |
| 10 years | How will I feel about it in 10 years? |
Purpose: Shifts perspective from short-term emotion to long-term impact. If all three horizons align — it's an easy call. If they conflict, the 10-year horizon should usually win.
Trigger Phrases
| Phrase | Action |
|---|---|
| "Help me decide between..." | Starts a structured comparison of options |
| "Pros and cons of..." | Generates a weighted pros/cons table |
| "Should I [X] or [Y]?" | Runs a decision matrix or opportunity cost analysis |
| "What am I not considering?" | Surfaces blind spots and hidden assumptions |
| "Run a pre-mortem on..." | Scenarios worst-case outcomes to de-risk the decision |
| "Prioritize these for me..." | Uses ICE or weighted scoring to rank options |
| "Help me think this through..." | Combines frameworks layered for clarity |
Step-by-Step Instructions
Step 1: Define the Decision Clearly
A fuzzy question gets a fuzzy answer. Be specific:
- ❌ "Should I change jobs?"
- ✅ "Should I accept the offer at Company X ($120k, hybrid, startup) or stay at my current role ($110k, remote, corporate)?"
Step 2: Identify the Decision Type
| Decision Type | Recommended Framework |
|---|---|
| Low stakes, 2 options | Pros & Cons (weighted) |
| Multiple options, many criteria | Weighted Decision Matrix |
| High risk, irreversible | Pre-mortem |
| Scarcity (time/money focus) | Opportunity Cost Frame |
| Prioritizing a long list | ICE Score |
| Emotional/personal | 10/10/10 Rule |
Step 3: Collect the Data
Gather:
- All realistic options (at least 2, rarely more than 5)
- All relevant criteria
- Objective data where possible (numbers, dates, facts)
- Subjective preferences (gut feel, values, identity)
Step 4: Apply the Framework
Run the framework step by step. Document scores, weights, and reasoning.
Step 5: Check for Bias
| Bias | Mitigation |
|---|---|
| Confirmation bias | Actively list reasons against your preferred option first |
| Recency bias | Consider decisions from 6+ months ago — does this feel different? |
| Sunk cost | "If I had no prior investment in this, would I still choose it?" |
| Status quo bias | "If this weren't the default, would I pick it?" |
Step 6: Decide and Commit
- If the data is clear → decide.
- If it's close (within 10% in weighted scoring) → go with gut feel or the more reversible option.
- Write down the decision and your reasoning — future you will thank you.
Step 7: Review the Outcome
After the decision plays out, revisit your framework. Did your weights reflect reality? Did you miss a criterion? Retrospect improves future decisions.
Examples
Example 1: Freelancer Deciding Between Two Clients
Input: "Should I take Client A ($5k, urgent, boring) or Client B ($3k, flexible, exciting project)?"
Process: Weighted Decision Matrix
Criteria Weight Client A Client B Income 4 9 (36) 5 (20) Enjoyment 3 3 (9) 9 (27) Time Pressure 2 3 (6) 9 (18) Portfolio Value 4 4 (16) 9 (36) Total 67 101 Result: Client B wins despite lower pay, because portfolio value and enjoyment outweigh the income gap.
Example 2: Solopreneur — "Should I Build Feature X?"
Input: "Should I prioritize building a mobile app or improving onboarding?"
Process: ICE + Pre-mortem
ICE:
- Mobile App: Impact 8, Confidence 4, Ease 2 → ICE = 64
- Onboarding: Impact 6, Confidence 8, Ease 8 → ICE = 384
Pre-mortem on mobile app decision: "We built the app but no one used it because onboarding was broken." → Clear signal to fix onboarding first.
Pro Tips
- Weight matters more than scores: Most people fight over scores (7 vs 8). Weights determine the real outcome. Spend time getting weights right.
- Add a "must-have" filter: Before any matrix, define non-negotiables. If an option fails a must-have, eliminate it immediately.
- The 80% rule: If you have 80% of the information you could possibly get, decide. Waiting for perfect information is a decision too — usually the wrong one.
- Revisit, don't regret: Write down why you decided. When doubt creeps in, re-read your reasoning. Trust past-you's process.
- Some decisions are reversible: Jeff Bezos calls these "Type 2 decisions." If you can reverse it cheaply, don't over-analyze. Make the call fast.
Version History
- 38e2523 Current 2026-07-05 19:37
