OverviewHistoryStatsSecurity
npx skills add ...
Documentation
SKILL.md
npx skills add paramchoudhary/resumeskills --skill tech-resume-optimizer
Optimize resumes for software engineering, PM, and technical roles
npx skills add paramchoudhary/resumeskills --skill tech-resume-optimizer
Use this skill when the user:
What Tech Recruiters Look For:
Include:
Don't Include:
Option 1: By Category
Option 2: By Proficiency (use carefully)
Option 3: Flat List (ATS-friendly)
Languages:
Frameworks/Libraries:
Databases:
Cloud/DevOps:
[Action Verb] + [Technical What] + [Scale/Impact] + [Technology Used]
Examples:
❌ Weak Technical Bullet:
✅ Strong Technical Bullet:
Scale:
Performance:
Efficiency:
Business:
Software Engineer:
Data Engineer:
DevOps/SRE:
Product Manager (Technical):
Critical for:
Do Include:
Don't Include:
Make sure your GitHub shows:
Project READMEs should include:
If you match their stack:
If you don't match exactly:
Tech resumes should support your interview:
When optimizing a tech resume:
Remember: Your resume must pass ATS AND impress technical recruiters.
For ATS:
For Tech Recruiters:
Languages: Python, JavaScript, TypeScript, Go, SQL
Frameworks: React, Node.js, Django, FastAPI
Databases: PostgreSQL, MongoDB, Redis, Elasticsearch
Cloud/Infrastructure: AWS (EC2, S3, Lambda, RDS), Docker, Kubernetes, Terraform
Tools: Git, JIRA, CI/CD, Datadog, GrafanaExpert: Python, React, PostgreSQL, AWS
Proficient: Go, TypeScript, MongoDB, Docker
Familiar: Rust, GraphQL, KubernetesSkills: Python, JavaScript, TypeScript, React, Node.js, Django, PostgreSQL, MongoDB, AWS, Docker, Kubernetes, Git- Worked on backend services
- Helped improve system performance
- Built features for the product- Architected microservices migration from monolith, reducing deployment time from 2 hours to 15 minutes and enabling independent team deployments
- Optimized PostgreSQL queries and implemented Redis caching, reducing API latency by 60% (from 500ms to 200ms) for 100K daily active users
- Built real-time notification system using WebSockets and AWS SNS, handling 1M+ messages daily with 99.9% delivery rate• Designed and implemented authentication service using OAuth 2.0 and JWT, securing 2M+ user accounts with zero security incidents
• Led migration to Kubernetes, achieving 99.99% uptime and reducing infrastructure costs by 35% ($200K annually)
• Mentored 3 junior engineers through code reviews and pair programming, improving team velocity by 25%• Built data pipeline processing 100M+ events daily using Apache Kafka and Spark, reducing data latency from hours to minutes
• Designed data warehouse schema in Snowflake, enabling self-service analytics for 50+ business users
• Implemented data quality monitoring with Great Expectations, catching 95% of data issues before impacting downstream systems• Implemented infrastructure as code using Terraform, reducing provisioning time from 2 days to 30 minutes
• Built monitoring and alerting system with Prometheus and Grafana, reducing MTTR from 4 hours to 30 minutes
• Automated deployment pipeline with GitHub Actions, enabling 50+ daily deployments with zero-downtime releases• Led API platform roadmap for developer tools used by 10K+ developers, driving 40% increase in API adoption
• Defined technical requirements for ML recommendation engine, resulting in 25% increase in user engagement
• Partnered with engineering to reduce technical debt by 30%, improving release velocity from bi-weekly to weeklyProject Name | Technologies | Link
• Description of what it does
• Technical highlights and challenges solved
• Scale or usage metrics if availablePROJECTS
Distributed Task Queue | Python, Redis, Docker | github.com/user/taskqueue
• Built distributed task queue handling 10K+ jobs/hour with automatic retries and dead letter queue
• Implemented priority queuing and rate limiting for multi-tenant support
Real-time Chat App | React, Node.js, WebSocket, MongoDB | chatapp.demo.com
• Full-stack chat application supporting 100+ concurrent users with real-time messaging
• Implemented end-to-end encryption and message persistence
ML Price Predictor | Python, TensorFlow, FastAPI | github.com/user/predictor
• Trained regression model on 1M+ data points achieving 92% accuracy for price prediction
• Deployed as REST API with automatic model retraining pipelineB.S. Computer Science | Stanford University | 2020
GPA: 3.8/4.0 (include if above 3.5)
Relevant Coursework: Distributed Systems, Machine Learning, Database SystemsSoftware Engineering Certificate | App Academy | 2023
- 1000+ hour immersive program
- Full-stack JavaScript, React, Node.js, PostgreSQL
B.A. Economics | UCLA | 2020Professional Certifications:
- AWS Solutions Architect Associate | 2023
- MongoDB Certified Developer | 2023
Relevant Education:
- MIT OpenCourseWare: Algorithms, Data Structures
- Coursera: Machine Learning Specialization (Stanford)# TECH RESUME OPTIMIZATION
## Technical Skills Restructure
**Current:** [Their current skills section]
**Optimized:**
Languages: [Ordered list]
Frameworks: [Ordered list]
Databases: [Ordered list]
Cloud/Tools: [Ordered list]
## Experience Improvements
### [Company/Role]
**Current Bullet 1:**
"Worked on backend services"
**Improved:**
"Designed and deployed 5 Node.js microservices handling 50K requests/minute, reducing system coupling and enabling independent team deployments"
**Current Bullet 2:**
[Continue for each bullet]
## Projects to Highlight
[Suggestions based on their background]
## GitHub Recommendations
- [ ] Add READMEs to pinned repos
- [ ] Pin X project (most relevant)
- [ ] Add profile README
## Technical Gaps to Address
- [Missing skill] → [How to address in resume/cover letter]