Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Objectively score and compare job candidates using customizable weighted criteria to support data-driven hiring decisions.
Objectively score and compare job candidates using customizable weighted criteria to support data-driven hiring decisions.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Score and compare job candidates objectively using weighted criteria. Eliminates gut-feel hiring decisions.
Tell your agent: "Score this candidate for [role]" or "Compare these 3 candidates for the backend engineer role."
Define the role โ provide job title and key requirements Set criteria โ the agent uses 6 default dimensions (or you customize): Technical skills (weight: 25%) Relevant experience (weight: 20%) Culture fit (weight: 15%) Communication (weight: 15%) Problem solving (weight: 15%) Growth potential (weight: 10%) Score candidates โ 1-5 scale per criterion after interview/review Get weighted totals โ ranked comparison with hire/no-hire recommendation
score candidate [name] for [role] โ start a new scorecard add criterion [name] weight [%] โ customize scoring dimensions compare candidates โ side-by-side ranked comparison hiring summary โ executive summary with recommendation
Score immediately after each interview while impressions are fresh Have multiple interviewers score independently, then compare Adjust weights per role (e.g., bump Technical to 40% for senior eng) Track scores over time to calibrate your hiring bar
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