Resume Optimizer

Problem to Solve

Job applications often fail when a resume has relevant experience but is written for a generic audience, or when formatting hurts ATS parsing. This skill turns resume optimization into a reviewable workflow for engineers and content creators: diagnose structure, relevance, quantification, keyword coverage, ATS compatibility, and language, then tailor content to a specific job description.

Capabilities and Key Steps

• /resume-diagnose: reads resume text or file paths such as .md, .docx, and .pdf, then scores structure, relevance, quantification, keyword density, ATS compatibility, language, and length on a 1-10 scale with a location-tagged issue list.
• /resume-tailor: extracts hard skills, soft skills, industry terms, and certification requirements from the JD, builds a JD keyword → original wording → optimized wording mapping, reorders the most relevant experience, rewrites a 3-5 line summary, and prioritizes the skills section.
• /resume-quantify: converts vague statements such as “participated in optimization” into measurable claims like 30% faster cycle time or served 100k+ users, while keeping strong verbs and result-first phrasing.
• /resume-polish and /resume-translate: improve grammar, tense, articles, prepositions, and bullet point style in Chinese and English; translations preserve company names, product names, proper nouns, and metrics instead of over-localizing.

Boundaries

• Works for Chinese, English, and bilingual resumes, including early-career, mid-career, career-change, and company-specific applications.
• Optimizes existing experience only; it does not invent projects, metrics, or facts, and it does not provide visual design or layout creation.
• Sensitive fields such as phone numbers, email addresses, and locations should be redacted before processing; keep resumes concise, typically one page for under five years of experience and two pages for ten or more.