About this skill

Problem

In restaurant and multi-option decisions, the hard part is often not listing candidates, but comparing them consistently. Differences in experience, cost, operations, risk, and budget are easy to discuss, but hard to rank. Intuition can bias the result and leave reviews without a reusable basis. This skill is aimed at multi-criteria evaluation tasks such as supplier selection, restaurant option comparison, store partnership screening, or scoring candidates against quantified dimensions.

How It Works

It structures the process into four steps: define the problem and data, match a weighted scoring framework, generate ranked options, and plan execution with review. Core capabilities include calculating multi-criteria weighted scores with scoring_matrix.py, loading relevant docs from references/ when needed, and using assets/ templates for checklists and analysis. Output is expected to lead with key numbers, then Top3 actions, a detailed plan, and usable templates. Missing data should be marked as baseline assumptions, and recommendations should specify who does what and the expected timeline.

Boundaries

It fits comparisons that can be broken into dimensions and weights, and that need an auditable trail. It is less suitable when judgment is purely subjective, data is too sparse, or weight agreement is absent. Prepare candidates, dimensions, and initial weights before use; treat the result as a decision starting point rather than a final verdict.

Screenshots

Use Cases

Compare restaurant suppliers or partner stores by scoring cost, fulfillment, and quality in one matrix.
Review multiple store expansion proposals by weighing experience, budget, and risk in a shared scoring model.
Compare technical options using weighted dimensions to produce Top3 recommendations and documented assumptions.
Audit supplier selection outcomes by rebuilding the decision matrix and recording assumptions with reusable templates.

Best For

Restaurant operations leads who need to rank suppliers by cost, fulfillment, and quality before partnership.
Data analysts who need to compare candidates in one scoring matrix and produce auditable, assumption-aware conclusions.
Product managers who need prioritized options, key numbers, and execution boundaries when comparing proposals.
Procurement or supply chain engineers who need a documented, multi-criteria scoring process for vendor evaluation.