Extract must-have terms, hard skills, and soft skills from a job description, with optional resume keyword coverage analysis.
Content last reviewed
0 words
With a resume, you also get a matched/missing density analysis.0 words
Uses 1 free daily AI generation. Nothing is stored after the analysis.
AI-generated content may contain inaccuracies. Text you submit for AI features is sent to the server-side AI provider for processing; please review and personalize the output before use.
Extracted keywords appear here
Grouped as hard skills, soft skills and must-haves — the recruiter's actual search terms.
Paste at least 30 characters from the full job description.
Paste resume text or upload a PDF, DOCX, or TXT file for coverage analysis.
Inspect must-haves, hard skills, soft skills, density findings, and the optional PDF report.
A posting can produce grouped terms rather than one undifferentiated list.
Input
Requires Python, SQL, and AWS experience. The candidate should communicate clearly and collaborate across teams. Five years of data engineering experience required.Output
Hard skills: Python, SQL, AWS, data engineering
Soft skills: communication, collaboration
Must-have: five years of data engineering experienceInput
Job description onlyOutput
Grouped must-have, hard-skill, and soft-skill listsInput
Job description plus optional resume textOutput
Grouped keywords plus resume density items and recommendationsJob descriptions mix core requirements, preferred qualifications, responsibilities, and general company language. This extractor organizes the supplied posting into must-have terms, hard skills, and soft skills. When resume text is included, it also requests keyword counts, status labels, and recommendations.
The output is AI-generated, so category boundaries and importance are interpretive. Use it to focus a manual reading of the posting, not to replace judgment or encourage keyword stuffing.
Paste the complete job description, including responsibilities and qualifications; at least 30 characters are required.
Optionally paste resume text or upload a text-based PDF, DOCX, or TXT file up to 5 MB.
Select Extract Keywords to send the text for AI analysis.
Review must-have keywords, hard skills, and soft skills. If a resume was supplied, inspect density counts, statuses, and recommendations.
Add only truthful, relevant terms to the resume and optionally download the report as a PDF.
Separates must-have qualifications, hard skills, and soft skills.
Adds keyword counts, missing or usage statuses, and recommendations.
Extracts text from supported resume files up to 5 MB.
Exports keyword groups and density results when available.
Find exact terminology worth reflecting when it truthfully matches experience.
Separate technical skills from interpersonal expectations and must-have requirements.
Check whether a resume explicitly mentions central terms from the posting.
Turn extracted requirements into topics for examples and preparation.
The endpoint always receives validated job-description text and optionally receives resume text. Without a resume it returns grouped keyword arrays and an unanalyzed density object. With a resume it also returns density items containing a keyword, count, and status—missing, under-used, good, or over-used—plus recommendations.
Pasted plain text up to 30,000 characters.
Optional pasted text or text-based PDF, DOCX, or TXT upload up to 5 MB.
Grouped on-page analysis and PDF report.
Extract grouped terms without resume density.
Include resume text for counts, statuses, and recommendations.
At least 30 characters are required.
Scanned PDFs need OCR before the server can extract text.
AI may place a term in a different group or miss contextual importance.
Keyword extraction uses the rate-limited AI service.
Use the full posting rather than a shortened excerpt. Review repeated responsibilities and qualification language manually as well.
Prefer clear evidence in relevant bullets. Do not repeat a keyword simply to change a status label.
Job-description and optional resume text are sent to the server-side AI provider during extraction. Resume files are sent to the server for text extraction. PDF export sends the result sections to the PDF endpoint.
Paste the full posting, not only the summary.
Prioritize repeated and explicitly required terms.
Use employer wording only when it accurately matches your experience.
Place skills in achievement context instead of creating a keyword list alone.
Identify the role's central outcomes yourself, then use the grouped result as a second view.
Add a term where it helps a reader understand real work. Remove repetition that makes the resume awkward or unconvincing.
Job-description input is validated from 30–30,000 characters and optional resume text up to 30,000. The AI response uses arrays for hard_skills, soft_skills, and must_have, plus a structured density object.
| Mode | Resume required | Additional result |
|---|---|---|
| Keyword extraction | No | Three grouped keyword lists |
| Coverage analysis | Yes | Counts, statuses, and recommendations |
Applicants tailoring a resume, career advisers reviewing job requirements, students learning the vocabulary of a target role, and interview candidates organizing preparation topics.
No. A job description alone produces grouped keywords. Resume text enables density and coverage analysis.
Text-based PDF, DOCX, and TXT files up to 5 MB are supported for server-side extraction.
No. Include only relevant terms that accurately describe your skills and experience.
They are AI-generated labels based on keyword counts in the supplied resume, not employer thresholds.
Yes. The result can be downloaded as a PDF.