name: resume-optimizer
version: 1.0.0
description: Build and optimize CVs/resumes with ATS scoring, keyword analysis, PDF/DOCX export, and multiple templates. Tailor resumes for specific job descriptions automatically.
tags: [resume, cv, ats, job-search, pdf, docx, career, optimization, latex]
author: garri333
license: MIT
source: https://clawdbotskills.org/
Resume Optimizer Skill
Setup
pip install python-docx reportlab spacy scikit-learn python-dotenv
python -m spacy download en_core_web_sm
Resume Data Structure
from dataclasses import dataclass, field
@dataclass
class ResumeContact:
name: str
email: str
phone: str
location: str
linkedin: str = ""
github: str = ""
website: str = ""
@dataclass
class ResumeExperience:
company: str
role: str
start: str # "Jan 2022"
end: str # "Present"
location: str
bullets: list[str] = field(default_factory=list)
@dataclass
class ResumeEducation:
institution: str
degree: str
field: str
start: str
end: str
gpa: str = ""
achievements: list[str] = field(default_factory=list)
@dataclass
class ResumeSkills:
technical: list[str] = field(default_factory=list)
languages: list[str] = field(default_factory=list)
tools: list[str] = field(default_factory=list)
soft: list[str] = field(default_factory=list)
@dataclass
class Resume:
contact: ResumeContact
summary: str
experience: list[ResumeExperience] = field(default_factory=list)
education: list[ResumeEducation] = field(default_factory=list)
skills: ResumeSkills = field(default_factory=ResumeSkills)
certifications: list[str] = field(default_factory=list)
projects: list[dict] = field(default_factory=list)
languages_spoken: list[dict] = field(default_factory=list) # [{"language": "Spanish", "level": "Native"}]
ATS Keyword Analysis
import re
from collections import Counter
import spacy
nlp = spacy.load("en_core_web_sm")
def extract_keywords(text: str, top_n: int = 30) -> list[str]:
"""Extract important keywords from a job description."""
doc = nlp(text.lower())
# Filter to nouns, proper nouns, adjectives — skip stopwords
keywords = [
token.lemma_ for token in doc
if not token.is_stop and not token.is_punct and token.pos_ in {"NOUN", "PROPN", "ADJ"}
and len(token.text) > 2
]
counter = Counter(keywords)
return [word for word, _ in counter.most_common(top_n)]
def ats_score(resume: Resume, job_description: str) -> dict:
"""Calculate ATS compatibility score against a job description."""
jd_keywords = set(extract_keywords(job_description, top_n=50))
# Build resume text blob
resume_text = " ".join([
resume.summary,
" ".join(resume.skills.technical),
" ".join(resume.skills.tools),
" ".join([b for exp in resume.experience for b in exp.bullets]),
]).lower()
resume_doc = nlp(resume_text)
resume_words = {token.lemma_ for token in resume_doc if not token.is_stop}
matched = jd_keywords & resume_words
missing = jd_keywords - resume_words
score = (len(matched) / len(jd_keywords)) * 100 if jd_keywords else 0
return {
"score": round(score, 1),
"matched_keywords": sorted(matched),
"missing_keywords": sorted(missing),
"recommendation": "Good ATS match" if score >= 70 else "Add missing keywords to improve ATS score"
}
def optimize_for_job(resume: Resume, job_description: str) -> dict:
"""Suggest bullet improvements to target a specific JD."""
score = ats_score(resume, job_description)
recommendations = []
if score["score"] < 60:
recommendations.append("⚠️ ATS score < 60%. You may be filtered automatically.")
missing = score["missing_keywords"][:10]
if missing:
recommendations.append(f"Add these keywords to bullets/skills: {', '.join(missing)}")
# Check quantification
all_bullets = [b for exp in resume.experience for b in exp.bullets]
quantified = sum(1 for b in all_bullets if any(c.isdigit() for c in b))
pct = (quantified / len(all_bullets) * 100) if all_bullets else 0
if pct < 50:
recommendations.append(f"Only {pct:.0f}% of bullets have numbers. Add metrics (%, $, #users, time saved).")
return {"ats_score": score, "recommendations": recommendations}
Bullet Point Improver
STRONG_VERBS = [
"Architected", "Automated", "Built", "Delivered", "Designed",
"Drove", "Engineered", "Increased", "Launched", "Led",
"Optimized", "Reduced", "Scaled", "Shipped", "Streamlined"
]
def improve_bullet(bullet: str) -> str:
"""
Template for improving a resume bullet.
Format: [Strong Verb] + [Task] + [Tool/Method] + [Quantified Result]
Before: "Helped with the development of new features"
After: "Engineered 12 new API endpoints using FastAPI, reducing response time by 40%"
"""
# Check if bullet starts with strong verb
first_word = bullet.split()[0].rstrip(",.")
if first_word not in STRONG_VERBS:
suggestion = f"Start with a stronger verb. Instead of '{first_word}', try one of: {', '.join(STRONG_VERBS[:5])}"
else:
suggestion = None
has_number = any(c.isdigit() for c in bullet)
has_percentage = "%" in bullet
feedback = {
"original": bullet,
"has_metric": has_number or has_percentage,
"strong_verb": first_word in STRONG_VERBS,
"suggestion": suggestion,
"template": "[Strong Verb] + [What you did] + [How/Tool] + [Result: X% / $X / X users]"
}
return feedback
def review_all_bullets(resume: Resume) -> list[dict]:
"""Review all bullets across all experiences."""
issues = []
for exp in resume.experience:
for bullet in exp.bullets:
feedback = improve_bullet(bullet)
if not feedback["has_metric"] or not feedback["strong_verb"]:
issues.append({
"company": exp.company,
"role": exp.role,
"bullet": bullet,
"has_metric": feedback["has_metric"],
"strong_verb": feedback["strong_verb"],
"suggestion": feedback["suggestion"],
})
return issues
Export to DOCX
from docx import Document
from docx.shared import Pt, Inches, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
def export_to_docx(resume: Resume, output_path: str = "resume.docx"):
"""Generate a clean ATS-friendly DOCX resume."""
doc = Document()
# Margins
for section in doc.sections:
section.top_margin = Inches(0.75)
section.bottom_margin = Inches(0.75)
section.left_margin = Inches(1.0)
section.right_margin = Inches(1.0)
def add_heading(text, level=1, color=(0, 0, 0)):
para = doc.add_paragraph()
run = para.add_run(text)
run.bold = True
run.font.size = Pt(14 if level == 1 else 12)
run.font.color.rgb = RGBColor(*color)
return para
def add_divider():
para = doc.add_paragraph()
para.add_run("─" * 70).font.size = Pt(8)
# Contact
contact_para = doc.add_paragraph()
contact_para.alignment = WD_ALIGN_PARAGRAPH.CENTER
run = contact_para.add_run(resume.contact.name)
run.bold = True
run.font.size = Pt(18)
doc.add_paragraph(
f"{resume.contact.email} | {resume.contact.phone} | {resume.contact.location}"
).alignment = WD_ALIGN_PARAGRAPH.CENTER
if resume.contact.linkedin or resume.contact.github:
links = " | ".join(filter(None, [resume.contact.linkedin, resume.contact.github]))
doc.add_paragraph(links).alignment = WD_ALIGN_PARAGRAPH.CENTER
# Summary
add_divider()
add_heading("PROFESSIONAL SUMMARY", color=(31, 73, 125))
doc.add_paragraph(resume.summary)
# Experience
add_divider()
add_heading("EXPERIENCE", color=(31, 73, 125))
for exp in resume.experience:
p = doc.add_paragraph()
p.add_run(exp.role).bold = True
p.add_run(f" — {exp.company}")
p2 = doc.add_paragraph()
p2.add_run(f"{exp.start} – {exp.end} | {exp.location}").italic = True
for bullet in exp.bullets:
doc.add_paragraph(bullet, style="List Bullet")
# Education
add_divider()
add_heading("EDUCATION", color=(31, 73, 125))
for edu in resume.education:
p = doc.add_paragraph()
p.add_run(f"{edu.degree} in {edu.field}").bold = True
p.add_run(f" — {edu.institution}")
doc.add_paragraph(f"{edu.start} – {edu.end}").italic = True
# Skills
add_divider()
add_heading("SKILLS", color=(31, 73, 125))
if resume.skills.technical:
doc.add_paragraph(f"Technical: {', '.join(resume.skills.technical)}")
if resume.skills.tools:
doc.add_paragraph(f"Tools: {', '.join(resume.skills.tools)}")
doc.save(output_path)
return output_path
Export to PDF (via reportlab)
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
from reportlab.lib.units import cm
from reportlab.lib import colors
def export_to_pdf(resume: Resume, output_path: str = "resume.pdf"):
"""Generate a clean PDF resume."""
doc = SimpleDocTemplate(output_path, pagesize=A4,
leftMargin=2*cm, rightMargin=2*cm,
topMargin=2*cm, bottomMargin=2*cm)
styles = getSampleStyleSheet()
story = []
# Name
name_style = ParagraphStyle("name", fontSize=20, fontName="Helvetica-Bold", alignment=1)
story.append(Paragraph(resume.contact.name, name_style))
story.append(Spacer(1, 0.3*cm))
# Contact line
contact_line = f"{resume.contact.email} | {resume.contact.phone} | {resume.contact.location}"
story.append(Paragraph(contact_line, ParagraphStyle("contact", alignment=1, fontSize=10)))
story.append(Spacer(1, 0.5*cm))
# Summary
story.append(Paragraph("PROFESSIONAL SUMMARY", styles["Heading2"]))
story.append(Paragraph(resume.summary, styles["Normal"]))
story.append(Spacer(1, 0.4*cm))
# Experience
story.append(Paragraph("EXPERIENCE", styles["Heading2"]))
for exp in resume.experience:
story.append(Paragraph(f"<b>{exp.role}</b> — {exp.company} ({exp.start}–{exp.end})", styles["Normal"]))
for b in exp.bullets:
story.append(Paragraph(f"• {b}", styles["Normal"]))
story.append(Spacer(1, 0.3*cm))
doc.build(story)
return output_path
Full Workflow Example
resume = Resume(
contact=ResumeContact(
name="Maria García",
email="maria@example.com",
phone="+34 600 000 000",
location="Madrid, Spain",
linkedin="linkedin.com/in/mariagarcia",
github="github.com/mariagarcia"
),
summary="Senior Backend Engineer with 5+ years building scalable APIs. Led teams of 4-8 engineers. Specialized in Python, FastAPI, and cloud-native architectures.",
experience=[
ResumeExperience(
company="TechCorp",
role="Senior Backend Engineer",
start="Jan 2022",
end="Present",
location="Madrid, Spain",
bullets=[
"Architected microservices platform handling 10M requests/day, reducing latency by 45%",
"Led migration from monolith to Kubernetes, saving €80K/year in infrastructure",
"Mentored 3 junior engineers, 2 of whom were promoted within 12 months"
]
)
],
education=[
ResumeEducation(
institution="Universidad Politécnica de Madrid",
degree="M.Sc.",
field="Computer Science",
start="2016",
end="2018"
)
],
skills=ResumeSkills(
technical=["Python", "FastAPI", "PostgreSQL", "Redis", "Kafka"],
tools=["Docker", "Kubernetes", "AWS", "Terraform", "GitHub Actions"]
)
)
jd = """
We are looking for a Senior Engineer with experience in Python, microservices,
Kubernetes, CI/CD pipelines, and cloud infrastructure. Experience with Terraform
and AWS required. Strong communication skills needed.
"""
# ATS Analysis
result = optimize_for_job(resume, jd)
print(f"ATS Score: {result['ats_score']['score']}%")
for rec in result["recommendations"]:
print(f" → {rec}")
# Bullet Review
issues = review_all_bullets(resume)
for issue in issues:
print(f" [{issue['company']}] {issue['bullet'][:60]}…")
if issue["suggestion"]:
print(f" → {issue['suggestion']}")
# Export
export_to_docx(resume, "maria_garcia_resume.docx")
export_to_pdf(resume, "maria_garcia_resume.pdf")
References
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