# Apply Cv Builder > Build, revise, and restructure academic CVs for PhD applications, research master's programs, fellowships, research internships, RA roles, and lab applications. Use this skill whenever the user asks to turn a resume into an academic CV, improve research-experience bullets, organize publications/projects/teaching/awards, identify missing evidence for research readiness, or make a CV fit research admissions expectations. - Skill: `jurgendn/apply-cv-builder` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add jurgendn/apply-cv-builder` - Raw SKILL.md: https://api.skillmd.com/api/skills/jurgendn/apply-cv-builder/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: jurgendn (https://skillmd.com/u/jurgendn) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/jurgendn/apply-cv-builder --- # Academic CV Builder An academic CV is an evidence document. It should make the applicant's research preparation legible: what they worked on, what methods they used, what outputs resulted, and what signal an admissions committee or PI can trust. ## Use this when - The user is applying to PhD programs, research master's programs, fellowships, research internships, RA roles, or labs. - The user wants to create, revise, or restructure an academic CV. - The user asks to convert an industry-style resume into a research-oriented CV. - The user needs stronger research-experience, project, publication, teaching, award, or technical-skill entries. - The user wants to identify missing or weak evidence in their research profile. ## Do not use this when - The user wants to write or revise a Statement of Purpose. Use `apply-sop-writer`. - The user wants faculty or program matching. Use `apply-program-fit-mapper`. - The user wants a full application-package risk audit. Use `apply-package-auditor`. - The user wants a purely industry resume optimized for recruiters rather than research admissions. - The user wants a **diagnosis** of an existing CV — what a committee will notice, per-experience decomposition, graded bullets — before anything is rewritten. Use `apply-cv-reviewer` first, then return here to act on its repairs. ## Workflow ### 1. Identify the application context Extract: - target degree, role, fellowship, or lab - field and subfield - application country or norm if relevant - current CV/resume material - publications, preprints, posters, talks, projects, code, datasets, awards, teaching, service, and technical skills - constraints such as page limits or required formats If the user has not provided their current material, ask for it or provide a fill-in template. **Establish the format requirements before drafting.** A CV rejected on format is never read for content. Ask for — or tell the user exactly where to find — the call's required-documents list, any mandatory template, any stated page limit, and whether the institution publishes its own CV template. `references/cv-format-and-required-documents.md` covers this, including where Europass is genuinely required (EU-funded calls, Italian *bandi* asking for *formato europeo*), how to report grades on their native scale without self-converting, and how to handle personal-data fields. On personal data specifically: include a photo, date of birth, or similar **only when the call or template asks for it**. Do not import industry-résumé conventions into an academic application, and do not tell the user that a given country "expects a photo" — that varies by document type, institution, and year, and asserting it can push discriminatory personal data into an application that never requested it. ### 2. Separate evidence from presentation Classify material into: - direct research evidence: projects, papers, preprints, theses, lab work, RA work - technical preparation: methods, tools, systems, datasets, experiments - communication evidence: posters, talks, writing, teaching - recognition: awards, grants, scholarships, selective programs - service and leadership: mentoring, reviewing, community work - weaker or ambiguous evidence that needs clearer framing Do not inflate claims. If an item is vague, mark what detail is needed. ### 3. Choose the CV structure Prefer an academic CV order such as: 1. Education 2. Research interests 3. Research experience 4. Publications, preprints, posters, or talks 5. Selected projects 6. Teaching or mentoring 7. Awards and honors 8. Technical skills 9. Service, leadership, or outreach 10. References if appropriate Adjust for the applicant's strongest evidence. For early applicants, strong projects may come before sparse publications. ### 4. Rewrite entries as research evidence For each research or project bullet, emphasize: - research question or goal - applicant's concrete contribution - methods, tools, or theory used - scale, dataset, experiment, proof, system, or evaluation - output: paper, preprint, poster, code, result, deployed tool, or learned finding - advisor, lab, or collaboration context when useful Avoid vague verbs like “worked on” unless the actual contribution is unclear. ### 5. Audit gaps and priorities Flag: - missing dates, advisors, titles, venues, links, or outcomes - unsupported claims of research interest - overlong industry details that crowd out research evidence - skills lists not tied to projects - weak ordering that hides the best signal - items that belong in the SOP rather than the CV Prioritize fixes that improve admissions signal, not cosmetic polish. ## Output format When auditing or building a CV, use: ```markdown ## Recommended CV structure - [Section order with rationale] ## Revised or example entries ### [Section] - [Edited bullet or entry] ## Missing information - [Specific detail needed] ## Research-readiness signal - Strong signals: ... - Weak or unclear signals: ... ## Priority fixes 1. [Highest-impact fix] 2. [Next fix] 3. [Next fix] ``` For a full rewrite, preserve the user's facts and mark uncertain details as `[confirm]` rather than inventing them. ## Quality bar A strong output makes the applicant's research evidence easier to evaluate without exaggeration. It should improve structure, specificity, and credibility while keeping boundaries clear between CV facts, SOP narrative, and program-fit claims.