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Decision Matrix

面向多选项复杂决策的结构化框架:引导用户列出选项、定义评估标准与权重、逐项打分,计算加权总分并生成热力图,进行 ±20% 权重敏感性分析、识别关键差异因素,最终输出含排序推荐与注意事项的决策报告。

harrylabsj
harrylabsj
热度 21.67
最近更新 2026-06-13 15:00:38
分类:数据依赖:内部调用大模型应用场景:报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add decision-matrix
Metadata
name
decision-matrix
slug
decision-matrix
description
Multi-factor weighted decision matrix with sensitivity analysis for hard choices.

Decision Matrix

Use this skill when the user faces a complex choice with multiple options and wants a systematic, quantified framework to evaluate trade-offs, test assumptions, and reach a defensible decision.

Good triggers

  • "I need to choose between two job offers — help me decide."
  • "Which car should I buy? Evaluate options quantitatively."
  • "Decide between renting and buying a home."
  • "Compare software vendors for my team."
  • "Should I relocate to City A or City B?"
  • "Help me make a structured decision about my career path."

Workflow

  1. List options. Ask the user for 3-5 alternatives. If more than 5, suggest pre-filtering to the top contenders.

  2. Define criteria. Ask the user to list the dimensions that matter for this decision. For each criterion, also capture which direction is better (higher = better, or lower = better). Examples: salary, commute time, growth potential, work-life balance, cost.

  3. Weight criteria (1-10). Ask the user to assign importance weight to each criterion. Normalize to sum-to-100 percentages for clarity. Optional: flag criteria weighted > 9 as potential dealbreakers.

  4. Score each option per criterion (1-10). Ask the user to rate each option against each criterion. If a criterion has objective data, suggest the score (e.g., salary in RMB scaled to 1-10).

  5. Compute weighted scores. Calculate:

    WeightedScore(option) = Σ( score(option, criterion_i) × weight_i )
    

    Display as a heatmap:

    Option Criterion 1 (w=30%) Criterion 2 (w=20%) ... Total
    A 8 6 7.2
    B 5 9 6.8
  6. Sensitivity analysis. For each criterion, vary the weight by ±20% and re-rank:

    • If the #1 option changes under any ±20% weight shift, flag as unstable
    • Report the "stress test" — which criteria cause the most rank volatility
    • Identify the tipping point: what weight change would flip rank 1 and 2
  7. Identify key differentiators. Find the criterion or criteria that most drive the ranking difference between the top 2 options. These are the "swing factors" the user should examine most carefully.

  8. Deliver decision report. Structure:

    • Summary — top-ranked option with total score
    • Heatmap table — original scores and weighted totals
    • Sensitivity analysis — stability flag, tipping point if any
    • Key differentiators — the 1-2 criteria separating the leaders
    • Recommendation — ranked list with rationale per option
    • Caveats — assumptions made, data gaps, subjective scores flagged

Sample prompt

decision-matrix evaluate --options "Offer A,Offer B,Offer C" --criteria "薪资:8,发展空间:9,稳定性:6"