Shortlist

Screen your resume the way the system and the recruiter will

Paste the job description and your resume as plain text. The screen pulls the JD’s keywords, checks how your resume parses, scores keyword coverage and placement, tests every bullet for proof, and marks up the text the way a recruiter’s highlighter would. See the research behind every check.

Start with the JD. You can edit the keyword list before scoring.
Fixed rules this screen enforces

The research behind every check

Each check in this screen maps to how ATS platforms and recruiters actually behave. Sources are linked so you can verify them.

1. Parse first, or nothing else counts

Applicant tracking systems work in three stages: they parse the file into fields, match it against the job description, then rank or surface candidates. A resume that fails parsing never reaches matching. Workday, used by roughly 40% of the Fortune 500, is the strictest parser and expects standard headings and chronological roles; a resume that parses cleanly there generally parses everywhere.

What the screen checks: standard headings (Experience, Education, Skills), email and phone in plain text, one date format, and no column or table artefacts.

Sources: ATS Verification, How ATS scoring works (2026) · ResumeGeni, ATS systems explained (2026) · SoftwareTestPilot, How Workday, Greenhouse and Lever parse

3. Keywords in context, never stuffed

Modern platforms penalise keyword dumps: a comma-separated block of every skill can lower ranking. Keywords inside work-experience descriptions carry more weight than the same words in a skills sidebar, and the Ladders eye-tracking study found recruiters also expect keywords to appear in context. Some systems, such as Ashby, group related tools into clusters, so naming several tools from the same family (where true) helps.

What the screen checks: whether each keyword is proven inside a bullet with a result, used without a result, only listed, or used more than 4 times.

Sources: Simplify, How to write an ATS resume (2026) · Blake Crosley, AI resume optimization · Ladders Eye-Tracking Study (2018)

4. Prove it with numbers: the XYZ formula

Laszlo Bock, Google’s former head of People Operations, recommended framing every accomplishment as “Accomplished [X] as measured by [Y] by doing [Z]”: open with an active verb, measure the result, give a baseline, and say how you did it. Numbers turn a list of duties into evidence a recruiter can compare.

What the screen checks: the share of bullets with a number (target 70%+), a strong opening verb, and very large percentages that need a defensible baseline.

Sources: Laszlo Bock, My personal formula for a winning resume (2014) · CNBC, Google recruiters’ 3-part formula

5. Assertive, specific language

A 2026 survey of 1,000 US hiring managers found most believe they can spot AI-written resumes, and the tells they named were repetitive language, vague descriptions and buzzword-heavy writing. Phrases like “responsible for”, “helped” and “worked on” describe presence, not impact, and repeated opening verbs make different achievements blur together.

What the screen checks: weak and filler phrases, opening verbs used more than twice, bullets that don’t open with an action verb, and bullets too long to skim.

Source: People Matters, on the Resume Genius 2026 Hiring Insights Report

6. Built for the seconds-long human scan

In the Ladders eye-tracking study, recruiters’ initial screen averaged 7.4 seconds. They looked at job titles more than anything else, and the resumes that held attention had simple layouts, clear section and title headers, and short declarative bullets rather than paragraphs. Cluttered, multi-column layouts performed worst.

What the screen checks: single-column structure, standard headers, bullet length, and an overall length that fits one to two pages.

Source: Ladders Eye-Tracking Study (2018) · HR Dive summary

7. Real experience only, because claims get verified

Verification is tightening. In Robert Half’s survey of 2,000+ US hiring managers, 65% said AI-enhanced resumes made skills harder to verify, and recruiters describe “AI padding”, bullets rewritten to hit the posting’s terms without real evidence, as something that passes the filter but tells them nothing. Checkr’s 2026 survey found 73% of companies had encountered AI-generated resumes, with background-check and assessment tools rising as the response.

What the screen does: flags missing must-haves rather than inventing them, and tells you to cover genuine gaps in the cover letter or interview.

Sources: Deel, The AI-generated resume crisis (2026) · Checkr, Recruitment Realities 2026

8. What the leading tailoring tools check

Jobscan builds its match rate from hard skills first, then education (when an advanced degree is required), job title and soft skills, and recommends aiming for 75% or higher rather than 100%. Resume Worded scores 20+ checks grouped into Impact, Brevity and Style, including quantified achievements, action verbs, passive voice, personal pronouns, buzzwords and bullet length, with 85+ treated as a good score. Teal pulls keywords from a posting’s core responsibilities and requirements, highlights where each one already appears, and lets you drop irrelevant terms; its own help pages note recruiters rarely scan keywords that way, so the score is a guide. RezScore reported that across 70,000+ graded resumes, A-grade resumes were shorter than C-grade ones.

What this screen adopted: hard skills weighted above soft skills, a job-title check, passive-voice and pronoun checks, a 75%+ match-rate target and 85+ overall, editable keyword lists, keyword-in-context highlighting, and the master-plus-tailored-copy workflow. What it deliberately skips: rewarding raw keyword frequency, because the platforms penalise stuffing.

Sources: Jobscan tutorial · Resume Worded score guide · Resume Worded checks · Teal Matching Mode · RezScore on brevity. These are vendors describing their own products; treat their success claims with care.