Resume Optimizer
AI-powered resume optimization service to help job seekers improve interview success rates. Core capabilities: ATS keyword matching analysis (identify job description keywords, match rate assessment); Achievement quantification (transform descriptive language into quantifiable results); Targeted optimization (adjust content focus based on target position); Structure optimization (clear information hierarchy, highlighted key points); Language refinement (use strong action verbs, remove redundancy). Output includes: original resume scoring and assessment, ATS keyword analysis, optimized resume version, key improvement comparisons, action items, interview preparation advice. Supports entry-level to executive positions, English and Chinese output. Example input: resume_content=Resume text or LinkedIn profile export, job_description=Senior Product Manager role at a SaaS company, target_role=Senior Product Manager, industry=SaaS, experience_level=mid, optimization_focus=[ATS keywords, quantified achievements], output_language=en. If the agent needs to ask a human for missing details, it must collect and submit them using the input schema fields: resume_content, job_description, optional target_role, optional industry, experience_level, optimization_focus, and output_language.
Description
AI-powered resume optimization service to help job seekers improve interview success rates. Core capabilities: ATS keyword matching analysis (identify job description keywords, match rate assessment); Achievement quantification (transform descriptive language into quantifiable results); Targeted optimization (adjust content focus based on target position); Structure optimization (clear information hierarchy, highlighted key points); Language refinement (use strong action verbs, remove redundancy). Output includes: original resume scoring and assessment, ATS keyword analysis, optimized resume version, key improvement comparisons, action items, interview preparation advice. Supports entry-level to executive positions, English and Chinese output. Example input: resume_content=Resume text or LinkedIn profile export, job_description=Senior Product Manager role at a SaaS company, target_role=Senior Product Manager, industry=SaaS, experience_level=mid, optimization_focus=[ATS keywords, quantified achievements], output_language=en. If the agent needs to ask a human for missing details, it must collect and submit them using the input schema fields: resume_content, job_description, optional target_role, optional industry, experience_level, optimization_focus, and output_language.
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