resume-bullet-extraction
Auto-invoke after task completion to generate powerful resume bullet points from completed work.
When & Why to Use This Skill
The Resume Bullet Extraction skill automates the transformation of completed technical tasks into high-impact, professional resume bullet points. By utilizing a structured formula—Action Verb + Task + Technical Context + Impact—it ensures that every achievement is documented with quantifiable metrics and technical depth, helping users build a powerful 'highlight reel' of their career impact in real-time.
Use Cases
- Post-Task Documentation: Automatically generating a professional achievement statement immediately after finishing a complex coding task or feature implementation.
- Quantifying Technical Impact: Translating abstract improvements, such as database refactoring, into specific metrics like 'reducing API response time by 70%' for recruiter visibility.
- STAR Method Preparation: Compiling a library of technical stories and bullets during a project to simplify future interview preparation and resume updates.
- Recruiter-Friendly Translation: Converting highly specialized technical work into concise, value-driven sentences that demonstrate business impact to non-technical hiring managers.
| name | Resume Bullet Extraction |
|---|---|
| description | Auto-invoke after task completion to generate powerful resume bullet points from completed work. |
Resume Bullet Extraction
"Your resume isn't a job description. It's a highlight reel of impact."
Purpose
Transform completed work into powerful resume bullet points that demonstrate value and technical competence.
The Bullet Formula
[Strong Action Verb] + [What You Did] + [Technical Context] + [Impact/Result]
Components
| Component | Purpose | Example |
|---|---|---|
| Action Verb | Shows initiative | Engineered, Architected, Optimized |
| What You Did | The accomplishment | JWT authentication system |
| Technical Context | Shows skill | using React, Node.js, Redis |
| Impact | Why it matters | reducing auth errors by 40% |
Strong Action Verbs
Building/Creating
- Engineered
- Architected
- Developed
- Implemented
- Built
- Designed
Improving
- Optimized
- Enhanced
- Refactored
- Modernized
- Streamlined
- Accelerated
Problem Solving
- Resolved
- Debugged
- Eliminated
- Reduced
- Prevented
- Mitigated
Leading/Collaborating
- Led
- Spearheaded
- Collaborated
- Mentored
- Coordinated
Impact Quantification
Always try to quantify. If you can't measure directly, estimate reasonably.
Performance
- "reducing load time by 60%"
- "improving response time from 2s to 200ms"
- "handling 10,000+ concurrent users"
Reliability
- "achieving 99.9% uptime"
- "eliminating production errors"
- "reducing bug reports by 50%"
Business
- "increasing user retention by 25%"
- "supporting 50,000 monthly active users"
- "saving 10 hours/week of manual work"
Scale
- "processing 1M+ transactions daily"
- "managing 500GB of user data"
- "serving 100+ API endpoints"
Bullet Templates
Feature Implementation
[Verb] [feature] using [technologies] that [impact]
Examples:
- Engineered JWT authentication with refresh token rotation using Node.js and Redis, eliminating session hijacking vulnerabilities
- Built real-time notification system using WebSockets and React, improving user engagement by 35%
Performance Optimization
[Verb] [what] by [how], resulting in [metric]
Examples:
- Optimized database queries through index analysis and query restructuring, reducing API response time by 70%
- Accelerated page load performance by implementing code splitting and lazy loading, improving Core Web Vitals by 40%
Bug Fix / Problem Solving
[Verb] [problem] by [solution], preventing [impact]
Examples:
- Resolved race condition in checkout flow by implementing optimistic locking, preventing duplicate charges
- Eliminated memory leak in React components through proper cleanup, reducing crash reports by 90%
Architecture / Refactoring
[Verb] [system] from [old] to [new], enabling [benefit]
Examples:
- Migrated monolithic application to microservices architecture using Docker and Kubernetes, enabling independent team deployments
- Refactored authentication module from session-based to JWT, reducing server memory usage by 60%
Quality Checklist
- Starts with strong action verb (not "Responsible for")
- Includes specific technologies
- Has quantifiable impact OR clear business value
- Is one concise sentence
- Avoids jargon recruiters won't understand
- Demonstrates ownership ("I" is implied)
- Would make sense to a technical interviewer
Bad vs Good Examples
Bad
❌ "Worked on the login system"
- No action verb, no specifics, no impact
❌ "Responsible for user authentication"
- Passive, no accomplishment shown
❌ "Helped with performance improvements"
- Vague, no ownership, no metrics
Good
✅ "Engineered JWT authentication with refresh token rotation, reducing session vulnerability surface and supporting 50,000+ daily active users"
✅ "Optimized PostgreSQL queries through index analysis, reducing average API response time from 800ms to 120ms"
✅ "Built responsive dashboard using React and D3.js, enabling real-time visualization of 1M+ daily events"
Extraction Flow
Step 1: Identify the Highlight
"What's the most impressive aspect of what you just built?"
Options:
- Technical complexity solved
- Business problem addressed
- Performance improved
- Scale achieved
- Security enhanced
Step 2: Draft the Bullet
Use the formula: Verb + What + Technical Context + Impact
Step 3: Quantify
"Can we add numbers? How much faster? How many users? What percentage improvement?"
Step 4: Polish
- Remove weak words ("helped", "assisted", "worked on")
- Add specific technologies
- Ensure it stands alone (no context needed)
Resume Section Placement
| Bullet Type | Resume Section |
|---|---|
| Feature/System built | Projects or Experience |
| Performance optimization | Experience (shows impact) |
| Architecture decision | Experience or Technical Skills |
| Learning/Growth | Skills or Side Projects |
Socratic Bullet Questions
- Finding impact: "If this feature didn't exist, what would break?"
- Quantifying: "How many users does this affect? How much time does it save?"
- Technical depth: "What would you tell a technical interviewer about how this works?"
- Differentiation: "What makes your implementation better than a basic solution?"
Save Location
Bullets are compiled in STAR story files:
mentorspec/career/stories/[date]-[feature-name].md
The resume bullet appears at the end of each story for easy extraction.