The Truth About in demand resume building salary negotiation: A Comprehensive Guide

import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport time\nimport json\nimport os\nimport logging\nfrom typing import List, Dict, Optional\n\n# Configure logging to track scrape progress and errors\nlogging.basicConfig(\n    level=logging.INFO,\n    format='%(asctime)s - %(levelname)s - %(message)s',\n    handlers=[logging.StreamHandler()]\n)\n\nclass JobScraper:\n    """Scrapes job postings from targeted job boards to extract in-demand skills."""\n    \n    def __init__(self, output_dir: str = "./scraped_jobs"):\n        self.output_dir = output_dir\n        self.headers = {\n            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'\n        }\n        # Create output directory if it doesn't exist\n        os.makedirs(output_dir, exist_ok=True)\n        self.scraped_jobs: List[Dict] = []\n        \n    def scrape_indeed(self, query: str, location: str, pages: int = 5) -> List[Dict]:\n        """\n        Scrape job postings from Indeed for a given query and location.\n        \n        Args:\n            query: Job title or keyword (e.g., "Senior Python Engineer")\n            location: City or state (e.g., "Remote")\n            pages: Number of search result pages to scrape (max 10 to avoid rate limiting)\n            \n        Returns:\n            List of job dictionaries with title, company, skills, and posting URL\n        """\n        base_url = "https://www.indeed.com/jobs"\n        for page in range(pages):\n            try:\n                params = {\n                    'q': query,\n                    'l': location,\n                    'start': page * 10,\n                    'sort': 'date'\n                }\n                logging.info(f"Scraping Indeed page {page + 1} for query: {query}")\n                response = requests.get(base_url, headers=self.headers, params=params, timeout=10)\n                response.raise_for_status()  # Raise HTTPError for bad responses (4xx, 5xx)\n                \n                soup = BeautifulSoup(response.text, 'html.parser')\n                job_cards = soup.find_all('div', class_='job_seen_beacon')\n                \n                if not job_cards:\n                    logging.warning(f"No job cards found on page {page + 1}, stopping early.")\n                    break\n                    \n                for card in job_cards:\n                    try:\n                        title_elem = card.find('h2', class_='jobTitle')\n                        title = title_elem.text.strip() if title_elem else "N/A"\n                        \n                        company_elem = card.find('span', class_='companyName')\n                        company = company_elem.text.strip() if company_elem else "N/A"\n                        \n                        url_elem = card.find('a', href=True)\n                        job_url = f"https://www.indeed.com{url_elem['href']}" if url_elem else "N/A"\n                        \n                        # Extract job description to parse skills\n                        desc_elem = card.find('div', class_='job-snippet')\n                        description = desc_elem.text.strip() if desc_elem else ""\n                        \n                        # Simple skill extraction (can be extended with NLP later)\n                        common_skills = ['python', 'java', 'golang', 'aws', 'kubernetes', 'docker', 'react', 'typescript', 'sql', 'git']\n                        found_skills = [skill for skill in common_skills if skill in description.lower()]\n                        \n                        job_data = {\n                            'title': title,\n                            'company': company,\n                            'url': job_url,\n                            'skills': found_skills,\n                            'source': 'indeed',\n                            'scraped_at': pd.Timestamp.now().isoformat()\n                        }\n                        self.scraped_jobs.append(job_data)\n                    except Exception as e:\n                        logging.error(f"Error parsing job card: {str(e)}")\n                        continue\n                        \n                # Respect rate limits: 2 seconds between pages\n                time.sleep(2)\n            except requests.exceptions.RequestException as e:\n                logging.error(f"HTTP error on page {page + 1}: {str(e)}")\n                continue\n            except Exception as e:\n                logging.error(f"Unexpected error on page {page + 1}: {str(e)}")\n                continue\n                \n        # Save scraped data to JSON\n        output_path = os.path.join(self.output_dir, f"indeed_{query.replace(' ', '_')}.json")\n        with open(output_path, 'w') as f:\n            json.dump(self.scraped_jobs, f, indent=2)\n        logging.info(f"Saved {len(self.scraped_jobs)} jobs to {output_path}")\n        return self.scraped_jobs\n\nif __name__ == "__main__":\n    # Example usage: Scrape senior Python engineer jobs in Remote\n    scraper = JobScraper()\n    jobs = scraper.scrape_indeed(query="Senior Python Engineer", location="Remote", pages=3)\n    print(f"Scraped {len(jobs)} total jobs.")\n

import pdfplumber\nimport docx\nfrom docx.shared import Pt, Inches\nfrom docx.enum.text import WD_ALIGN_PARAGRAPH\nimport pandas as pd\nimport json\nimport os\nimport logging\nfrom typing import List, Dict, Optional\nfrom collections import Counter\n\nlogging.basicConfig(\n    level=logging.INFO,\n    format='%(asctime)s - %(levelname)s - %(message)s'\n)\n\nclass ResumeOptimizer:\n    """Optimizes resumes to match in-demand skills from job scrapes, ensures ATS compatibility."""\n    \n    def __init__(self, resume_path: str, scraped_jobs_path: str):\n        self.resume_path = resume_path\n        self.scraped_jobs_path = scraped_jobs_path\n        self.resume_text: str = ""\n        self.top_skills: List[str] = []\n        self.optimized_resume_path: str = "optimized_resume.docx"\n        \n    def extract_resume_text(self) -> str:\n        """Extract text from PDF or DOCX resume."""\n        try:\n            if self.resume_path.endswith('.pdf'):\n                with pdfplumber.open(self.resume_path) as pdf:\n                    self.resume_text = "\n".join([page.extract_text() for page in pdf.pages if page.extract_text()])\n            elif self.resume_path.endswith('.docx'):\n                doc = docx.Document(self.resume_path)\n                self.resume_text = "\n".join([para.text for para in doc.paragraphs])\n            else:\n                raise ValueError("Unsupported resume format. Use PDF or DOCX.")\n            logging.info(f"Extracted {len(self.resume_text)} characters from resume.")\n            return self.resume_text\n        except FileNotFoundError:\n            logging.error(f"Resume file not found at {self.resume_path}")\n            raise\n        except Exception as e:\n            logging.error(f"Error extracting resume text: {str(e)}")\n            raise\n        \n    def get_top_skills(self, top_n: int = 10) -> List[str]:\n        """Extract top N most in-demand skills from scraped job data."""\n        try:\n            with open(self.scraped_jobs_path, 'r') as f:\n                jobs = json.load(f)\n            all_skills = [skill for job in jobs for skill in job.get('skills', [])]\n            skill_counts = Counter(all_skills)\n            self.top_skills = [skill for skill, count in skill_counts.most_common(top_n)]\n            logging.info(f"Top {top_n} in-demand skills: {self.top_skills}")\n            return self.top_skills\n        except FileNotFoundError:\n            logging.error(f"Scraped jobs file not found at {self.scraped_jobs_path}")\n            raise\n        except Exception as e:\n            logging.error(f"Error extracting top skills: {str(e)}")\n            raise\n        \n    def optimize_resume(self, output_path: Optional[str] = None) -> str:\n        """\n        Generate an ATS-friendly optimized resume with top skills highlighted.\n        Returns path to optimized resume.\n        """\n        output_path = output_path or self.optimized_resume_path\n        try:\n            # Create new DOCX document (ATS-friendly: no images, standard fonts)\n            doc = docx.Document()\n            style = doc.styles['Normal']\n            style.font.name = 'Arial'\n            style.font.size = Pt(11)\n            \n            # Add header with contact info (placeholder: replace with real data)\n            header = doc.add_paragraph()\n            header.add_run("John Doe\n").bold = True\n            header.add_run("Senior Software Engineer\n")\n            header.add_run("john.doe@email.com | (123) 456-7890 | linkedin.com/in/johndoe | github.com/johndoe")\n            header.alignment = WD_ALIGN_PARAGRAPH.CENTER\n            \n            # Add Skills section with top in-demand skills\n            doc.add_heading('Technical Skills', level=1)\n            skills_para = doc.add_paragraph()\n            # Highlight skills present in resume, add missing top skills\n            existing_skills = [skill for skill in self.top_skills if skill in self.resume_text.lower()]\n            missing_skills = [skill for skill in self.top_skills if skill not in self.resume_text.lower()]\n            if existing_skills:\n                skills_para.add_run(f"Existing Top Skills: {', '.join(existing_skills)}\n")\n            if missing_skills:\n                skills_para.add_run(f"Added In-Demand Skills: {', '.join(missing_skills)}\n")\n            # Add all top skills to ensure ATS picks them up\n            skills_para.add_run(f"All Top Skills: {', '.join(self.top_skills)}")\n            \n            # Add Work Experience section (simplified: parse existing resume's experience)\n            doc.add_heading('Work Experience', level=1)\n            # Extract experience section from original resume (simplified)\n            if "experience" in self.resume_text.lower():\n                exp_start = self.resume_text.lower().find('experience')\n                exp_text = self.resume_text[exp_start:exp_start + 2000]  # Truncate to 2000 chars\n                doc.add_paragraph(exp_text)\n            else:\n                doc.add_paragraph("Add your work experience here, tailored to include top skills.")\n                \n            # Save document\n            doc.save(output_path)\n            logging.info(f"Optimized resume saved to {output_path}")\n            return output_path\n        except Exception as e:\n            logging.error(f"Error optimizing resume: {str(e)}")\n            raise\n\nif __name__ == "__main__":\n    # Example usage\n    optimizer = ResumeOptimizer(\n        resume_path="my_resume.pdf",\n        scraped_jobs_path="./scraped_jobs/indeed_Senior_Python_Engineer.json"\n    )\n    optimizer.extract_resume_text()\n    optimizer.get_top_skills(top_n=10)\n    optimizer.optimize_resume()\n

import requests\nimport pandas as pd\nimport json\nimport os\nimport logging\nfrom typing import Dict, List, Optional\nfrom datetime import datetime\n\nlogging.basicConfig(\n    level=logging.INFO,\n    format='%(asctime)s - %(levelname)s - %(message)s'\n)\n\nclass SalaryNegotiator:\n    """Calculates market salary range and generates negotiation talking points using real compensation data."""\n    \n    # Levels.fyi public API endpoint (unofficial, use with rate limiting)\n    LEVELS_API = "https://www.levels.fyi/api/v1/compensation?company=all&title={title}&location={location}&year={year}"\n    \n    def __init__(self, title: str, location: str, years_experience: int, current_salary: Optional[float] = None):\n        self.title = title\n        self.location = location\n        self.years_experience = years_experience\n        self.current_salary = current_salary\n        self.market_data: List[Dict] = []\n        self.salary_range: Dict[str, float] = {}\n        \n    def fetch_market_data(self, year: int = datetime.now().year) -> List[Dict]:\n        """\n        Fetch real compensation data from Levels.fyi for the given title and location.\n        Note: Unofficial API, subject to change. Use sparingly to avoid rate limits.\n        """\n        try:\n            url = self.LEVELS_API.format(title=self.title.replace(' ', '%20'), location=self.location.replace(' ', '%20'), year=year)\n            logging.info(f"Fetching market data from Levels.fyi: {url}")\n            response = requests.get(url, timeout=10)\n            response.raise_for_status()\n            data = response.json()\n            self.market_data = data.get('data', [])\n            logging.info(f"Fetched {len(self.market_data)} compensation records.")\n            return self.market_data\n        except requests.exceptions.RequestException as e:\n            logging.error(f"HTTP error fetching market data: {str(e)}")\n            # Fallback to local sample data if API fails\n            self.market_data = self._load_sample_data()\n            logging.info(f"Using sample data: {len(self.market_data)} records.")\n            return self.market_data\n        except Exception as e:\n            logging.error(f"Unexpected error fetching market data: {str(e)}")\n            self.market_data = self._load_sample_data()\n            return self.market_data\n        \n    def _load_sample_data(self) -> List[Dict]:\n        """Load sample compensation data if API is unavailable."""\n        return [\n            {"baseSalary": 150000, "stockSalary": 50000, "bonusSalary": 20000, "totalComp": 220000},\n            {"baseSalary": 160000, "stockSalary": 60000, "bonusSalary": 25000, "totalComp": 245000},\n            {"baseSalary": 170000, "stockSalary": 70000, "bonusSalary": 30000, "totalComp": 270000},\n            {"baseSalary": 180000, "stockSalary": 80000, "bonusSalary": 35000, "totalComp": 295000},\n            {"baseSalary": 190000, "stockSalary": 90000, "bonusSalary": 40000, "totalComp": 320000},\n        ]\n        \n    def calculate_salary_range(self) -> Dict[str, float]:\n        """Calculate 25th, 50th, 75th percentile total compensation ranges."""\n        if not self.market_data:\n            raise ValueError("No market data available. Fetch data first.")\n        total_comps = [record.get('totalComp', 0) for record in self.market_data if record.get('totalComp', 0) > 0]\n        if not total_comps:\n            raise ValueError("No valid total compensation data found.")\n        df = pd.DataFrame(total_comps, columns=['total_comp'])\n        self.salary_range = {\n            'p25': df['total_comp'].quantile(0.25),\n            'p50': df['total_comp'].quantile(0.50),\n            'p75': df['total_comp'].quantile(0.75),\n            'average': df['total_comp'].mean(),\n            'min': df['total_comp'].min(),\n            'max': df['total_comp'].max()\n        }\n        logging.info(f"Salary range calculated: {self.salary_range}")\n        return self.salary_range\n    \n    def generate_negotiation_talking_points(self) -> List[str]:\n        """Generate data-backed talking points for salary negotiation."""\n        if not self.salary_range:\n            self.calculate_salary_range()\n        talking_points = []\n        \n        # Point 1: Market value\n        talking_points.append(\n            f"Based on 2024 Levels.fyi data for {self.title} roles in {self.location}, "\n            f"the market average total compensation is ${self.salary_range['average']:,.2f}, "\n            f"with the 75th percentile at ${self.salary_range['p75']:,.2f}. "\n            f"My {self.years_experience} years of experience align with the top 25% of candidates."\n        )\n        \n        # Point 2: Current compensation gap\n        if self.current_salary:\n            gap = self.salary_range['p50'] - self.current_salary\n            if gap > 0:\n                talking_points.append(\n                    f"My current total compensation is ${self.current_salary:,.2f}, "\n                    f"which is ${gap:,.2f} below the market median. "\n                    f"I’m looking for a package that reflects my market value and contributions."\n                )\n            else:\n                talking_points.append(\n                    f"My current total compensation is ${self.current_salary:,.2f}, "\n                    f"which is above the market median. I’m looking for a role that offers "\n                    f"${self.salary_range['p75']:,.2f} total comp to match my experience."\n                )\n        \n        # Point 3: Value add\n        talking_points.append(\n            f"In my previous role, I led the migration of our monolithic architecture to microservices, "\n            f"reducing deployment time by 60% and saving the company $120k annually. "\n            f"I plan to bring this same impact to your team, justifying a compensation package "\n            f"of ${self.salary_range['p75']:,.2f} total."\n        )\n        \n        # Point 4: Flexibility\n        talking_points.append(\n            f"If the base salary is constrained, I’m open to negotiating equity, signing bonus, "\n            f"or remote work flexibility to reach the total compensation target of ${self.salary_range['p75']:,.2f}."\n        )\n        \n        logging.info(f"Generated {len(talking_points)} talking points.")\n        return talking_points\n\nif __name__ == "__main__":\n    # Example usage: Senior Python Engineer in Remote, 8 years experience\n    negotiator = SalaryNegotiator(\n        title="Senior Python Engineer",\n        location="Remote",\n        years_experience=8,\n        current_salary=180000\n    )\n    negotiator.fetch_market_data()\n    negotiator.calculate_salary_range()\n    talking_points = negotiator.generate_negotiation_talking_points()\n    for i, point in enumerate(talking_points, 1):\n        print(f"Talking Point {i}: {point}\n")\n

import subprocess\nimport json\nfrom datetime import datetime, timedelta\n\ndef get_quantifiable_commits(repo_path: str, days: int = 90) -> list:\n    since_date = (datetime.now() - timedelta(days=days)).isoformat()\n    cmd = f"git -C {repo_path} log --since={since_date} --pretty=format:'%H|%s|%an' --numstat"\n    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)\n    # Parse output to count lines added/removed per commit\n    return [line for line in result.stdout.split('\n') if line.strip()]\n

def check_ats_issues(resume_text: str) -> list:\n    issues = []\n    if any(char in resume_text for char in ['•', '—', '“', '”']):\n        issues.append("Special characters detected: replace with standard ASCII equivalents")\n    if 'table' in resume_text.lower():\n        issues.append("Tables detected: ATS systems often misparse table content")\n    if any(font in resume_text.lower() for font in ['calibri', 'times new roman']):\n        issues.append("Non-standard font detected: use Arial or Helvetica")\n    return issues\n

def calculate_total_comp(base: float, equity: float, bonus: float, benefits: float = 0) -> dict:\n    total = base + equity + bonus + benefits\n    return {\n        'base': base,\n        'equity': equity,\n        'bonus': bonus,\n        'benefits': benefits,\n        'total': total,\n        'monthly': total / 12\n    }\n