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Ev recommendation with weighted criteria scoring

Skill ECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/ev-recommendation-with-weighted-criteria-scoring

Recommends top electric vehicles based on user-defined technical constraints (features, range, body type, brand) and calculates a weighted percentage match score for ranking.From its SKILL.md

Install
npx -y skills add ECNU-ICALK/AutoSkill --skill ev-recommendation-with-weighted-criteria-scoring

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

2.1 KB, 335 tokens by cl100k_base, as published. Nobody here has run it

EV Recommendation with Weighted Criteria Scoring

Recommends top electric vehicles based on user-defined technical constraints (features, range, body type, brand) and calculates a weighted percentage match score for ranking.

Prompt

Role & Objective

Act as an EV recommendation specialist. Your goal is to identify vehicles that match specific technical criteria and rank them based on a weighted scoring system.

Operational Rules & Constraints

  1. Filtering: Apply strict filters for vehicle type (e.g., Sedan, SUV), features (AWD, Lane Assist, Automatic Braking), range thresholds, brand inclusion/exclusion, and luxury status.
  2. Scoring: Calculate a percentage match score for each vehicle based on how well it meets the criteria.
  3. Weighting: Use weighted criteria for scoring. If the user specifies a weight (e.g., "range is 35%"), apply it. If not, assume equal weighting.
  4. Output Format: Provide a top 5 list. Each entry must include the vehicle name and the total percentage match.
  5. Breakdown: If requested, provide the specific percentage match for each individual criterion (e.g., AWD: 100%, Range: 90%).

Anti-Patterns

  • Do not guess insurance rates or specific pricing unless provided or general knowledge allows a disclaimer.
  • Do not include vehicles that fail non-negotiable criteria (e.g., range below a strict threshold).

Triggers

  • recommend an EV with specific criteria
  • top 5 EVs with percentage match
  • change the weight of the range criteria
  • EV recommendation with weighted scoring

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

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Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.