Resume Tailoring

AI-powered resume tailoring — gap analysis, ATS keywords, bullet reordering and rewriting. Every change grounded in existing content; no fabrication.

Source Repository

• GitHub: varunr89/resume-tailoring-skill
• Install upstream: npx skills add varunr89/resume-tailoring-skill

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/tailor

Paste (or point to) resume + job description.

Workflow:

1. Parse the JD — required skills, seniority signals, keywords ATS will scan.
2. Map existing experience to requirements.
3. Rewrite bullets to lead with most relevant impact (action → scope → result).
4. Inject missing keywords only where honestly supported by existing experience.
5. Output tailored resume + gap analysis report.

Gap analysis report includes:

• Requirements fully covered
• Partial matches needing stronger framing
• Genuine gaps to address in cover letter or upskilling
• Suggested talking points for interviews

Example prompts:

• Tailor my resume [paste] for this job description [paste]. Emphasize relevant experience, add missing keywords, flag gaps for my cover letter.
• I'm applying for Staff Engineer. Elevate framing from IC execution to technical leadership, system thinking, cross-team impact.
• Moving from data analyst to PM — reframe analytical work as product thinking; note transferable skills and gaps.

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/batch

Processes 3–5 job descriptions in one run — uniquely tailored variant per JD.

Output:

• One tailored resume per role
• Diff summary per variant (what changed and why)
• Optional: best-fit role ranking and largest-gap role

Example prompts:

• Base resume [paste] + 4 job descriptions [paste all 4]. Generate tailored variants with diff summary per version.
• Run batch on 3 PM roles. After tailoring, tell me which fits best and which has the largest gap.
• After tailoring [resume + JD], generate 10 likely interview questions with suggested talking points.

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Output Formats

Deliver in the format requested:

• Markdown (default)
• DOCX (use docx skill if available)
• PDF (use pdf skill if available)

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Guardrails

Zero fabrication policy:

• Never invent employers, titles, dates, metrics, or skills not in the source resume.
• Reframe and emphasize — do not fabricate.
• When a JD keyword has no honest match, flag it in the gap analysis instead of inserting it.
• Preserve the user's voice; avoid generic corporate filler.