ats-resume-checker 0.2.0

ATS resume analyzer for keyword matching and scoring

ats-resume-checker

A lightweight Python package that scores resumes against job descriptions for ATS (Applicant Tracking System) compatibility — runs fully locally, no external AI APIs required.

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Features

• Multi-format support — PDF, DOCX, and plain TXT resumes
• TF-IDF similarity scoring — more accurate than simple word-count matching
• Smart keyword extraction — lemmatization, bigrams, and a built-in library of 30+ recognized multi-word skills ("machine learning", "rest api", "data analysis", etc.)
• Keyword gap analysis — shows exactly which JD keywords are present or missing in the resume
• Keyword match rate — percentage of job description keywords covered
• Resume word count — flags resumes that are too short or too long
• Actionable suggestions — concrete tips to improve ATS compatibility
• CLI — run analysis directly from your terminal without writing any code

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Installation

pip install ats-resume-checker

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Quick Start

from ats_resume_checker import analyze_resume

result = analyze_resume(
    "resume.pdf",   # supports .pdf, .docx, and .txt
    "Looking for a Python developer with SQL and machine learning experience."
)

print(result)

Output

{
    "ats_score": 54.30,           # TF-IDF cosine similarity score (0–100)
    "match_rate": 66.7,           # % of JD keywords found in the resume
    "matched_keywords": [
        "data analysis",
        "machine learning",
        "python",
        "sql"
    ],
    "missing_keywords": [
        "communication",
        "developer",
        "problem solving"
    ],
    "resume_word_count": 520,
    "suggestions": [
        "Include more job-specific keywords. Missing: communication, developer, problem solving.",
        "Quantify achievements where possible (e.g. 'Improved performance by 30%')."
    ]
}

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Python API

analyze_resume(resume_path, job_description)

Parameter	Type	Description
resume_path	str	Path to the resume file (.pdf, .docx, or .txt)
job_description	str	Plain-text job description

Returns a dict with ats_score, match_rate, matched_keywords, missing_keywords, resume_word_count, suggestions.

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Utility functions

from ats_resume_checker import extract_text, extract_keywords, get_keyword_set

# Extract text from any supported file type
text = extract_text("resume.pdf")

# Get a ranked list of keywords (includes bigrams and known phrases)
keywords = extract_keywords(text, top_n=30)

# Get a clean keyword set (unigrams + known phrases only, no noise)
kw_set = get_keyword_set(text, top_n=50)

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Command-Line Interface

After installation the ats-check command is available in your terminal:

# Job description as a string
ats-check resume.pdf "Python developer with SQL and machine learning skills"

# Job description as a .txt file
ats-check resume.docx job_description.txt

# Output as JSON (useful for scripts and pipelines)
ats-check resume.pdf "Data engineer with Spark experience" --json

Example output

==============================================
   ATS RESUME ANALYSIS REPORT
==============================================
  ATS Score       : 54.30%
  Keyword Match   : 66.7%
  Resume Words    : 520

  Matched Keywords (4):
    + data analysis
    + machine learning
    + python
    + sql

  Missing Keywords (3):
    - communication
    - developer
    - problem solving

  Suggestions:
    * Include more job-specific keywords. Missing: communication, developer, problem solving.
    * Quantify achievements where possible.
==============================================

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Dependencies

Package	Purpose
PyPDF2	PDF text extraction
python-docx	DOCX text extraction
scikit-learn	TF-IDF vectorization and cosine similarity
nltk	Lemmatization, stopwords, tokenization

All dependencies are installed automatically with pip install ats-resume-checker.

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License

MIT © Vidhi Bhutia