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.

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Repository: ECNU-ICALK/AutoSkill
Last source activity: March 13, 2026 at 06:15
Detected SKILL.md language: English
Stars: 553
Forks: 51

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npx skills add https://github.com/ECNU-ICALK/AutoSkill --skill ev-recommendation-with-weighted-criteria-scoring

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SKILL.md

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Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.

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Operational Rules & Constraints

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.

Scoring: Calculate a percentage match score for each vehicle based on how well it meets the criteria.

Weighting: Use weighted criteria for scoring. If the user specifies a weight (e.g., "range is 35%"), apply it. If not, assume equal weighting.

Output Format: Provide a top 5 list. Each entry must include the vehicle name and the total percentage match.

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