Ranks discovered candidate roles by CV fit and preferences with reasons and recommendations.
npx skills add https://github.com/jvdheyden/jobwatch --skill rank-jobs
Please help me install this Agent Skill. Skill: rank-jobs Source: https://github.com/jvdheyden/jobwatch/tree/main/.agents/skills/rank-jobs Command: npx skills add https://github.com/jvdheyden/jobwatch --skill rank-jobs
Helps job seekers evaluate and prioritize candidate roles by scoring fit against the CV and track preferences, reducing time spent on low-value opportunities.
Rank the discovered candidate roles against the user's CV and track preferences to produce a prioritized list.
High-intent search queries and answers about installing and using this skill.
The ranking mechanism scores each candidate role on a 1-10 scale based on CV and preference alignment. It outputs two ordered lists, top_matches and other_new_roles, sorted strongest to weakest to prioritize your job search.
Prioritize job listings by applying a standardized scoring mechanism across all discovered roles based on evidence from postings and your profile. This approach yields concrete recommendations like apply_now, watch, or skip for each role.
Yes, scoring roles records concrete reasons for fit and concerns alongside each score. The output explicitly reflects available evidence from postings and your profile, justifying the assigned recommendation for every candidate role.
Automatically assess job matches by scoring each role's alignment with your track preferences on a 1-10 scale. The system evaluates available evidence from postings against your profile to generate an explicit fit score and recommendation.
Yes, automated job ranking separates results into two distinct outputs: top_matches and other_new_roles. Both lists are ordered strongest to weakest, allowing you to quickly identify high-value opportunities and audit the decision process.
The limitation of automated job scoring is that results depend entirely on available evidence from postings and your CV profile. If source listings lack detail or preferences are vague, the accuracy of the 1-10 fit score and recommendation may decrease.
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