resume-manager | SkillBundle

Resume Manager

Overview

This skill transforms Claude into a comprehensive resume management system that maintains a structured database of your professional profile and generates tailored, professionally styled PDF resumes for specific job applications. The skill intelligently selects and highlights the most relevant experiences, projects, and skills based on the target role.

When to Use This Skill

Invoke this skill for resume-related tasks:

Creating tailored resumes for job applications
Updating professional experiences and projects
Managing skills and certifications
Tracking career progression
Generating role-specific resumes
Maintaining a comprehensive career portfolio
Optimizing resume content for ATS systems

Workflow

Step 1: Check for Existing Data

Before any resume operations, check if the database is initialized:

python3 scripts/resume_db.py is_initialized

If output is "false", proceed to Step 2 (Initial Setup). If "true", proceed to Step 3 (Resume Operations).

Step 2: Initial Setup - Extract from Existing Resume

When no data exists, ask the user to provide their existing resume.

Prompt the User:

To help you create tailored resumes, I need to build a database of your professional
profile. Please provide your existing resume in one of these ways:

1. Upload your resume file (PDF, DOCX, or TXT)
2. Paste the content of your resume
3. Provide a link to your online resume/LinkedIn profile

I'll extract all the information and organize it in a structured database that I can
use to generate customized resumes for different job applications.

Extracting Data from Resume:

Once the user provides their resume, extract the following information:

1. Personal Information:

Full name
Email address
Phone number
Location (city, state/country)
LinkedIn profile URL
GitHub profile URL
Personal website
Professional summary/objective

2. Work Experience: For each role, extract:

Position/Job title
Company name
Location
Start date (format: "Mon YYYY" like "Jan 2022")
End date (or "Present")
Brief description
Key highlights/achievements (bullet points)
Technologies/tools used

3. Projects: For each project, extract:

Project name
Date or time period
Description
Key highlights/achievements
Technologies used
Link (if available)

4. Education: For each degree, extract:

Degree name (e.g., "Bachelor of Science in Computer Science")
School/University name
Location
Graduation date
GPA (if mentioned)
Honors (if any)
Relevant coursework

5. Skills: Extract and categorize skills:

Programming Languages
Frameworks & Libraries
Tools & Technologies
Practices & Methodologies
Soft skills

6. Additional Sections:

Certifications (name, issuer, date)
Awards & Honors
Publications
Volunteer work
Languages spoken

Saving the Extracted Data:

After extraction, save to the database using Python:

Replace [SKILL_DIR] with the actual skill directory path.

Confirmation:

Step 3: Generate Tailored Resume for Job Application

When a user requests a resume for a specific role:

Step 3.1: Understand the Target Role

Ask the user about the role:

Step 3.2: Extract Keywords and Requirements

From the job description, identify:

Required technical skills
Preferred technologies
Key responsibilities
Important keywords for ATS
Industry-specific terms
Experience level indicators

Step 3.3: Generate Tailored Resume

Use the PDF generator to create a customized resume:

The generator will:

Filter experiences relevant to the keywords
Select projects that match the role
Highlight applicable skills
Keep it to one page
Use professional styling
Optimize for ATS parsing

Step 3.4: Review and Iterate

After generating:

Inform the user where the PDF was saved
Offer to make adjustments
Suggest additional highlights if space allows
Recommend customizations for specific requirements

Step 4: Update Resume Database

When users want to add or update information:

Adding New Experience:

Adding New Project:

Updating Skills:

Adding Certification:

Step 5: View and Manage Resume Data

View Summary:

View Specific Sections:

Search Across All Data:

Export All Data:

Step 6: Resume Optimization Tips

When generating resumes, provide these optimization tips:

Content Optimization:

Use action verbs (Led, Built, Architected, Improved, Reduced)
Include quantifiable metrics (X% improvement, Y users, Z revenue)
Tailor highlights to match job requirements
Keep bullet points concise (1-2 lines max)
Focus on impact, not just responsibilities

ATS Optimization:

Use standard section headers (Experience, Education, Skills)
Include keywords from job description naturally
Avoid tables, columns, headers/footers
Use standard fonts (which the PDF generator does)
Spell out acronyms once: "Application Programming Interface (API)"

One-Page Strategy:

Prioritize recent and relevant experience (last 5-7 years)
Include 3-4 bullet points per role
Limit to 2-3 projects maximum
Condense older or less relevant experience
Remove outdated technologies unless specifically required

Best Practices

Keep Data Current
- Update experiences as projects complete
- Add new skills as you learn them
- Maintain quantifiable achievements
- Record certifications immediately
Write Effective Bullet Points
- Start with action verbs
- Include metrics and outcomes
- Show progression and impact
- Use the STAR method (Situation, Task, Action, Result)
Organize Skills Strategically
- Group by category for clarity
- Put most relevant skills first
- Remove outdated technologies
- Be honest about proficiency levels
Tailor Every Resume
- Match keywords to job description
- Emphasize relevant experience
- Reorder bullet points by relevance
- Adjust technical skills section
Maintain Professional Tone
- Use third-person implied (no "I")
- Keep language concise and clear
- Avoid jargon unless industry-standard
- Proofread for errors

Example Interactions

Example 1: Initial Setup

Example 2: Generate Resume for Specific Role

Example 3: Update Experience

Example 4: Multiple Resume Versions

Example 5: Resume Review and Optimization

Technical Notes

Data Storage:

Location: ~/.claude/resume_data.json
Format: Structured JSON
Backup: Use python3 scripts/resume_db.py export

PDF Generation:

Library: reportlab (requires: pip install reportlab)
Page size: US Letter (8.5" x 11")
Margins: 0.75 inches all sides
Font: Helvetica family
Optimized for: One-page resumes, ATS compatibility

Resume Styling:

Professional color scheme (blues and grays)
Clear section headers with underlines
Consistent spacing and formatting
Bullet points for achievements
Contact info in header
Technical skills as comma-separated lists

Database Commands:

PDF Generation Commands:

Data Structure Example:

Resources

scripts/resume_db.py

Complete database management system providing:

Data initialization and persistence
CRUD operations for all resume sections
Relevance-based filtering for experiences/projects
Keyword-based skill matching
Search functionality across all data
Data export and backup
CLI interface for all operations

scripts/pdf_generator.py

Professional PDF generation engine:

ReportLab-based PDF creation
Custom styling matching professional standards
One-page optimization
Keyword-based content filtering
Relevance scoring for experiences/projects
ATS-friendly formatting
Command-line interface

assets/resume_template.json

Sample resume data structure showing:

Complete data format
Best practices for content
Example bullet points with metrics
Proper date formatting
Skill categorization
All supported sections

Resume Manager

Overview

When to Use This Skill

Invoke this skill for resume-related tasks:

Creating tailored resumes for job applications
Updating professional experiences and projects
Managing skills and certifications
Tracking career progression
Generating role-specific resumes
Maintaining a comprehensive career portfolio
Optimizing resume content for ATS systems

Workflow

Step 1: Check for Existing Data

Before any resume operations, check if the database is initialized:

python3 scripts/resume_db.py is_initialized

If output is "false", proceed to Step 2 (Initial Setup). If "true", proceed to Step 3 (Resume Operations).

Step 2: Initial Setup - Extract from Existing Resume

When no data exists, ask the user to provide their existing resume.

Prompt the User:

To help you create tailored resumes, I need to build a database of your professional
profile. Please provide your existing resume in one of these ways:

1. Upload your resume file (PDF, DOCX, or TXT)
2. Paste the content of your resume
3. Provide a link to your online resume/LinkedIn profile

I'll extract all the information and organize it in a structured database that I can
use to generate customized resumes for different job applications.

Extracting Data from Resume:

Once the user provides their resume, extract the following information:

1. Personal Information:

Full name
Email address
Phone number
Location (city, state/country)
LinkedIn profile URL
GitHub profile URL
Personal website
Professional summary/objective

2. Work Experience: For each role, extract:

Position/Job title
Company name
Location
Start date (format: "Mon YYYY" like "Jan 2022")
End date (or "Present")
Brief description
Key highlights/achievements (bullet points)
Technologies/tools used

3. Projects: For each project, extract:

Project name
Date or time period
Description
Key highlights/achievements
Technologies used
Link (if available)

4. Education: For each degree, extract:

Degree name (e.g., "Bachelor of Science in Computer Science")
School/University name
Location
Graduation date
GPA (if mentioned)
Honors (if any)
Relevant coursework

5. Skills: Extract and categorize skills:

Programming Languages
Frameworks & Libraries
Tools & Technologies
Practices & Methodologies
Soft skills

6. Additional Sections:

Certifications (name, issuer, date)
Awards & Honors
Publications
Volunteer work
Languages spoken

Saving the Extracted Data:

After extraction, save to the database using Python:

Replace [SKILL_DIR] with the actual skill directory path.

Confirmation:

Step 3: Generate Tailored Resume for Job Application

When a user requests a resume for a specific role:

Step 3.1: Understand the Target Role

Ask the user about the role:

Step 3.2: Extract Keywords and Requirements

From the job description, identify:

Required technical skills
Preferred technologies
Key responsibilities
Important keywords for ATS
Industry-specific terms
Experience level indicators

Step 3.3: Generate Tailored Resume

Use the PDF generator to create a customized resume:

The generator will:

Filter experiences relevant to the keywords
Select projects that match the role
Highlight applicable skills
Keep it to one page
Use professional styling
Optimize for ATS parsing

Step 3.4: Review and Iterate

After generating:

Inform the user where the PDF was saved
Offer to make adjustments
Suggest additional highlights if space allows
Recommend customizations for specific requirements

Step 4: Update Resume Database

When users want to add or update information:

Adding New Experience:

Adding New Project:

Updating Skills:

Adding Certification:

Step 5: View and Manage Resume Data

View Summary:

View Specific Sections:

Search Across All Data:

Export All Data:

Step 6: Resume Optimization Tips

When generating resumes, provide these optimization tips:

Content Optimization:

Use action verbs (Led, Built, Architected, Improved, Reduced)
Include quantifiable metrics (X% improvement, Y users, Z revenue)
Tailor highlights to match job requirements
Keep bullet points concise (1-2 lines max)
Focus on impact, not just responsibilities

ATS Optimization:

Use standard section headers (Experience, Education, Skills)
Include keywords from job description naturally
Avoid tables, columns, headers/footers
Use standard fonts (which the PDF generator does)
Spell out acronyms once: "Application Programming Interface (API)"

One-Page Strategy:

Prioritize recent and relevant experience (last 5-7 years)
Include 3-4 bullet points per role
Limit to 2-3 projects maximum
Condense older or less relevant experience
Remove outdated technologies unless specifically required

Best Practices

Keep Data Current
- Update experiences as projects complete
- Add new skills as you learn them
- Maintain quantifiable achievements
- Record certifications immediately
Write Effective Bullet Points
- Start with action verbs
- Include metrics and outcomes
- Show progression and impact
- Use the STAR method (Situation, Task, Action, Result)
Organize Skills Strategically
- Group by category for clarity
- Put most relevant skills first
- Remove outdated technologies
- Be honest about proficiency levels
Tailor Every Resume
- Match keywords to job description
- Emphasize relevant experience
- Reorder bullet points by relevance
- Adjust technical skills section
Maintain Professional Tone
- Use third-person implied (no "I")
- Keep language concise and clear
- Avoid jargon unless industry-standard
- Proofread for errors

Example Interactions

Example 1: Initial Setup

Example 2: Generate Resume for Specific Role

Example 3: Update Experience

Example 4: Multiple Resume Versions

Example 5: Resume Review and Optimization

Technical Notes

Data Storage:

Location: ~/.claude/resume_data.json
Format: Structured JSON
Backup: Use python3 scripts/resume_db.py export

PDF Generation:

Library: reportlab (requires: pip install reportlab)
Page size: US Letter (8.5" x 11")
Margins: 0.75 inches all sides
Font: Helvetica family
Optimized for: One-page resumes, ATS compatibility

Resume Styling:

Professional color scheme (blues and grays)
Clear section headers with underlines
Consistent spacing and formatting
Bullet points for achievements
Contact info in header
Technical skills as comma-separated lists

Database Commands:

PDF Generation Commands:

Data Structure Example:

Resources

scripts/resume_db.py

Complete database management system providing:

Data initialization and persistence
CRUD operations for all resume sections
Relevance-based filtering for experiences/projects
Keyword-based skill matching
Search functionality across all data
Data export and backup
CLI interface for all operations

scripts/pdf_generator.py

Professional PDF generation engine:

ReportLab-based PDF creation
Custom styling matching professional standards
One-page optimization
Keyword-based content filtering
Relevance scoring for experiences/projects
ATS-friendly formatting
Command-line interface

assets/resume_template.json

Sample resume data structure showing:

Complete data format
Best practices for content
Example bullet points with metrics
Proper date formatting
Skill categorization
All supported sections

import sys import json sys.path.append('[SKILL_DIR]/scripts') from resume_db import initialize_from_data resume_data = { "personal_info": { "name": "Full Name", "email": "email@example.com", "phone": "+1 (555) 123-4567", "location": "City, State", "linkedin": "linkedin.com/in/username", "github": "github.com/username", "website": "website.com", "summary": "Professional summary..." }, "experiences": [ { "position": "Senior Software Engineer", "company": "Company Name", "location": "City, State", "start_date": "Jan 2022", "end_date": "Present", "description": "Brief description", "highlights": [ "Achievement 1 with quantifiable results", "Achievement 2 with impact metrics", "Achievement 3 with technologies used" ], "technologies": ["Python", "AWS", "Docker"] } ], "projects": [ { "name": "Project Name", "date": "2023", "description": "Project description", "highlights": [ "Key achievement or feature", "Impact or result" ], "technologies": ["React", "Node.js", "PostgreSQL"], "link": "github.com/username/project" } ], "education": [ { "degree": "Bachelor of Science in Computer Science", "school": "University Name", "location": "City, State", "graduation_date": "May 2019", "gpa": "3.8/4.0", "honors": "Magna Cum Laude", "relevant_coursework": ["Data Structures", "Algorithms", "Machine Learning"] } ], "skills": { "Languages": ["Python", "JavaScript", "Java"], "Frameworks": ["React", "Django", "Spring"], "Tools": ["Docker", "AWS", "Git"], "Practices": ["Agile", "CI/CD", "TDD"] }, "certifications": [ { "name": "AWS Certified Solutions Architect", "issuer": "Amazon Web Services", "date": "2023" } ], "awards": [], "publications": [], "volunteer": [], "languages": ["English (Native)", "Spanish (Fluent)"], "interests": [] } initialize_from_data(resume_data)