feat: add Tiny Resume Keyword Checker

#1316_Tiny_Resume_Keyword_Checker/resume_keyword_checker.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+"""
+Tiny Resume Keyword Checker
+Compare resume text with a job description and score keyword matches.
+
+Usage:
+    python resume_keyword_checker.py resume.txt job_description.txt
+"""
+
+import re
+import sys
+from collections import Counter
+from typing import List, Tuple
+
+
+# Common English stop words that carry little signal in a job description
+STOP_WORDS = {
+    "a", "an", "the", "and", "or", "but", "if", "of", "in", "on", "at",
+    "to", "for", "with", "by", "as", "is", "are", "was", "were", "be",
+    "been", "being", "have", "has", "had", "do", "does", "did", "will",
+    "would", "can", "could", "should", "may", "might", "must", "not",
+    "no", "yes", "so", "than", "then", "that", "this", "these", "those",
+    "it", "its", "we", "you", "they", "he", "she", "from", "about",
+    "into", "over", "after", "before", "between", "under", "up", "down",
+    "out", "off", "just", "all", "any", "each", "some", "such", "only",
+    "also", "very", "more", "most", "other", "own", "both", "same",
+}
+
+
+def tokenize(text: str) -> List[str]:
+    """Split text into lowercase word tokens."""
+    return re.findall(r"[a-z0-9][a-z0-9+\-\.]*", text.lower())
+
+
+def extract_keywords(job_text: str) -> List[str]:
+    """Extract meaningful keywords from a job description.
+
+    Returns words that appear at least twice in the description (so the
+    most emphasized skills surface), excluding stop words and single
+    characters.
+    """
+    tokens = [t for t in tokenize(job_text) if t not in STOP_WORDS and len(t) > 1]
+    counts = Counter(tokens)
+    # Keywords the posting repeats, plus anything that looks like a skill term
+    repeated = {w for w, c in counts.items() if c >= 2}
+    # Include single-mention words that are longer (e.g. "kubernetes")
+    long = {w for w, c in counts.items() if c == 1 and len(w) >= 6}
+    return sorted(repeated | long)
+
+
+def score_resume(resume_text: str, keywords: List[str]) -> Tuple[List[str], List[str], float]:
+    """Match resume text against keywords.
+
+    Returns (matched, missing, score) where score is the fraction of
+    keywords found in the resume (0.0 - 1.0).
+    """
+    resume_words = set(tokenize(resume_text))
+    matched = [k for k in keywords if k in resume_words]
+    missing = [k for k in keywords if k not in resume_words]
+    if not keywords:
+        return [], [], 0.0
+    return matched, missing, len(matched) / len(keywords)
+
+
+def main(argv: List[str]) -> int:
+    if len(argv) != 3:
+        print(__doc__)
+        return 2
+
+    resume_path, job_path = argv[1], argv[2]
+    try:
+        resume_text = open(resume_path, encoding="utf-8").read()
+        job_text = open(job_path, encoding="utf-8").read()
+    except OSError as e:
+        print(f"Error reading input files: {e}", file=sys.stderr)
+        return 1
+
+    keywords = extract_keywords(job_text)
+    matched, missing, score = score_resume(resume_text, keywords)
+
+    print(f"Keywords extracted from job description: {len(keywords)}")
+    print(f"Matched in resume: {len(matched)}/{len(keywords)}")
+    print(f"Score: {score:.0%}")
+    print()
+    if matched:
+        print("Matched:")
+        for k in matched:
+            print(f"  + {k}")
+    if missing:
+        print("Missing:")
+        for k in missing:
+            print(f"  - {k}")
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv))