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Decision Matrix

作者 haidong · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ pending
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在 OpenClaw 中安装
/install decision-matrix
功能描述
Multi-factor weighted decision matrix with sensitivity analysis for hard choices.
使用说明 (SKILL.md)

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"
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install decision-matrix
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /decision-matrix 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release: Multi-factor weighted decision matrix with sensitivity analysis
元数据
Slug decision-matrix
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Decision Matrix 是什么?

Multi-factor weighted decision matrix with sensitivity analysis for hard choices. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 21 次。

如何安装 Decision Matrix?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install decision-matrix」即可一键安装,无需额外配置。

Decision Matrix 是免费的吗?

是的,Decision Matrix 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Decision Matrix 支持哪些平台?

Decision Matrix 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Decision Matrix?

由 haidong(@harrylabsj)开发并维护,当前版本 v1.0.0。

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