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 audit 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
sop-writer. - The user wants faculty or program matching. Use
phd-program-fit-mapper. - The user wants a full application-package risk audit. Use
phd-application-package-auditor. - The user wants a purely industry resume optimized for recruiters rather than research admissions.
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.
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:
- Education
- Research interests
- Research experience
- Publications, preprints, posters, or talks
- Selected projects
- Teaching or mentoring
- Awards and honors
- Technical skills
- Service, leadership, or outreach
- 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:
## 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.