Resume Synthesizer Skill

Resume Synthesizer Skill

Purpose

Create a polished, professional resume by intelligently combining structured career data from multiple years into a coherent narrative.

Task

Generate RESUME.md by synthesizing:

• what_i_did_*.md files (all years)
• my_thoughts_*.md files (all years)
• performance_*.md files (all years)
• basic_info.md (static info: name, contact, education, military, certs)

Instructions

Step 1: Discover and Read All Components

• Use Glob to find all what_i_did_*.md, my_thoughts_*.md, performance_*.md files
• Read basic_info.md for static info (name, contact, education, military, certs, career)
• Sort by year (most recent first)

Step 2: Analyze Content with LLM Intelligence

For each year's data:

• Identify themes: What were the major accomplishments?
• Find patterns: Career progression, skill evolution, increasing impact
• Extract highlights: Most impressive projects, biggest wins
• Connect dots: How do learnings translate to results?

Step 3: Synthesize Resume Structure

Header

• Name, title, contact info (from basic_info.md)
• One-line value proposition (synthesized from overall career arc)

Professional Summary (3-4 sentences)

Synthesize from all years:

• Years of experience
• Core expertise areas (from what_i_did files)
• Key strengths (from my_thoughts files)
• Signature achievements (from performance files)

Example:

Senior Software Engineer with 6+ years building scalable AI/​ML systems. Led backend migrations improving query performance by 40%, deployed real-time streaming systems processing 10K+ events/​sec, and architected cloud infrastructure serving 100K+ users. Deep expertise in Python, Go, distributed systems, and MLOps, with proven ability to translate complex technical challenges into business value.

Technical Skills

Aggregate from all what_i_did_*.md files:

• Languages: Python, Go, JavaScript, etc.
• Frameworks: Django, FastAPI, React, etc.
• AI/​ML: TensorFlow, PyTorch, LangChain, etc.
• Infrastructure: Docker, Kubernetes, AWS, etc.
• Databases: PostgreSQL, MongoDB, Redis, etc.

Group logically, prioritize by recency and proficiency.

Work Experience

Synthesize from all three file types:

• Format: Company | Role | Dates
• Content: For each role/​year:
  • 3-5 bullet points per year
  • Start with impact (performance) → action (what_i_did) → context (my_thoughts)
  • Use strong action verbs (Led, Architected, Delivered, Optimized)
  • Quantify everything from performance files

Example:

## Work Experience

### Senior Software Engineer | Current Company | 2020 - Present

**2024**
- Led backend migration from PostgreSQL to MongoDB, reducing query latency by 40% and improving system scalability
- Architected real-time streaming mosaic processing system handling 10K+ concurrent CCTV streams
- Mentored 3 junior engineers on distributed systems design patterns learned through production challenges

**2023**
- Designed and deployed Naver Cloud tagging system processing 100K+ resources with 99.9% uptime
- Reduced infrastructure costs by 25% through automated resource optimization and monitoring
- Developed expertise in cloud-native architectures and multi-cloud deployment strategies

Key Projects (Optional section)

If there are standout projects that deserve spotlight:

• Select top 3-5 most impressive projects across all years
• Provide brief description + impact metrics
• Use when projects are more notable than chronological experience

Education & Certifications

From basic_info.md - keep concise.

Step 4: Apply Professional Polish

Tone:

• Confident, results-oriented
• Active voice, strong verbs
• Professional but not stiff

Language:

• Korean for narrative (if Profile is in Korean)
• English for technical terms
• Consistent terminology

Formatting:

• Clean markdown with clear hierarchy
• Consistent bullet point style
• Proper spacing and readability

Step 5: Quality Checks

Before writing output:

• ✅ All metrics from performance files included
• ✅ No redundancy or repetition
• ✅ Chronological order (recent first)
• ✅ Learnings from my_thoughts integrated naturally
• ✅ Projects from what_i_did accurately represented
• ✅ No grammatical errors
• ✅ Consistent formatting

Step 6: Write Output

Write to RESUME.md in the base directory.

Synthesis Principles

DO:

• Tell a story: Career progression should be clear
• Show impact: Every bullet point should demonstrate value
• Be specific: "Improved performance by 40%" not "Made system faster"
• Connect learnings to results: "Applied distributed systems patterns learned in Q1 to architect..."
• Highlight growth: Show increasing responsibility and impact over time

DON'T:

• Copy-paste from source files verbatim
• Include every single detail (be selective)
• Use generic phrases ("Worked on various projects")
• Forget to quantify achievements
• Lose the human element (learnings and growth)

Example Synthesis

Input Files:

what_i_did_2024.md: "Led backend migration to MongoDB" performance_2024.md: "Query latency reduced by 40%, handled 10K QPS" my_thoughts_2024.md: "Learned NoSQL data modeling, understood trade-offs"

Synthesized Output:

- Architected and led critical backend migration from PostgreSQL to MongoDB, applying NoSQL data modeling principles to achieve 40% latency reduction while scaling to 10K+ queries per second

Notice how it:

• Combines all three sources
• Leads with action and impact
• Weaves in learnings naturally
• Quantifies results
• Shows technical depth

Customization Options

You may receive additional instructions like:

• "Focus on leadership aspects" → Emphasize mentoring, architecture decisions
• "Technical depth preferred" → Include more technology details, design patterns
• "One-page format" → Be more selective, condensed bullets
• "For startup role" → Emphasize rapid iteration, scrappiness, breadth

Adapt synthesis strategy accordingly using your LLM judgment.

Success Criteria

• Resume is coherent and reads like a unified narrative
• All key achievements from performance files are highlighted
• Career growth is evident
• Technical skills are accurately represented
• Professional, polished, ready to send