Resume Extractor

Resume Extractor Skill

Purpose

Transform raw yearly career documents into three structured markdown files that separate facts from reflections from metrics.

Task

Given a year (e.g., 2024), process all related markdown files in that year's directory and extract:

1. what_i_did_YYYY.md - Factual accomplishments

   • Projects delivered
   • Technologies used
   • Systems built/maintained
   • Responsibilities held
   • Concrete deliverables

2. my_thoughts_YYYY.md - Personal growth & reflections

   • What I learned
   • Challenges faced and how I overcame them
   • Skills developed
   • Areas of growth
   • Key insights or "aha" moments

3. performance_YYYY.md - Quantifiable impact

   • KPIs and metrics
   • Business outcomes
   • Performance improvements (latency, throughput, revenue, etc.)
   • Team impact (people mentored, processes improved)
   • Recognition or achievements

Instructions

Step 1: Discover Source Files

• Use Glob to find all markdown files in the year directory (e.g., ./2024/*.md)
• Read each file to understand the content structure
• Note: Files may be quarterly reviews, project timelines, retrospectives, or summaries

Step 2: Intelligent Extraction

For each source file, use your LLM capabilities to:

• Understand context: Is this a quarterly review? A project description? A retrospective?
• Categorize content: Which sections belong to what_i_did vs my_thoughts vs performance?
• Handle ambiguity: Some content may fit multiple categories - use your judgment
• Preserve specifics: Keep dates, numbers, project names, technology names exact

Step 3: Structure Output

Each output file should be well-organized with:

• Clear section headers (## Projects, ## Technologies, ## Learnings, etc.)
• Chronological ordering (Q1 → Q4)
• Consistent formatting
• Deduplication (if same achievement mentioned multiple times)

Step 4: Validation

Before writing output files, verify:

• No information loss (all important details captured)
• No duplication across the three files
• Dates and metrics are accurate
• Proper markdown formatting

Step 5: Write Output

Write the three files to the same directory as the source files:

• YYYY/what_i_did_YYYY.md
• YYYY/my_thoughts_YYYY.md
• YYYY/performance_YYYY.md

Example Input/Output

Input: 2024/1분기.md

# 2024 Q1 회고

이번 분기에는 백엔드 시스템 마이그레이션을 주도했다.
PostgreSQL에서 MongoDB로 전환하면서 쿼리 성능이 40% 개선되었다.
이 과정에서 NoSQL 데이터 모델링에 대해 깊이 배울 수 있었다.

Output: 2024/what_i_did_2024.md

## Q1 - Projects
- Led backend system migration from PostgreSQL to MongoDB
- Redesigned data models for NoSQL architecture

Output: 2024/performance_2024.md

## Q1 - Impact
- Query performance improved by 40% after migration

Output: 2024/my_thoughts_2024.md

## Q1 - Learnings
- Gained deep understanding of NoSQL data modeling principles
- Learned trade-offs between relational and document databases

Error Handling

If you encounter:

• Missing files: Report which year directory has no content
• Ambiguous content: Make best judgment and note uncertainty in comments
• Invalid formats: Parse what you can, skip malformed sections
• Encoding issues: Try UTF-8, then fallback to other encodings

Success Criteria

• All three output files created
• Content properly categorized
• No information lost from source files
• Consistent formatting and structure