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Optimize resumes for software engineering, PM, and technical roles
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Based on SOC occupation classification
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Research-focused query handling with multi-source synthesis, citations, and Obsidian persistence. Like a self-hosted Perplexity/Vane but CLI-native. Best for quick-to-medium lookups using Kagi. Use when the user asks factual questions, needs citations, or wants a direct answer — not a full research report (use deep-research) or social sentiment (use last30days). Triggers on: research, look into, what's the latest on, compare, explain, investigate.
Write articles, guides, blog posts, tutorials, newsletter issues, research reports, and deep research outputs in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, deep research on a topic, or a research report — especially when voice consistency, structure, and credibility matter. Triggers on: 'write an article', 'research report', 'deep research', 'long-form', 'blog post', 'guide', 'newsletter', 'white paper', 'research paper'.
Systematic research methodology for major consumer durables (appliances, HVAC, power tools, outdoor equipment) and smart garden/outdoor devices (bird feeder cameras, bird baths, smart outdoor gadgets). Emphasis on real reliability data, failure mode analysis, and head-to-head comparison. Use when the user asks to research, review, compare, or evaluate major purchases where longevity and repair risk matter, or when researching smart bird feeders, bird bath cameras, and similar connected outdoor devices. Triggers on: washer/dryer, refrigerator, dishwasher, HVAC, furnace, AC, generator, power tool, appliance reviews, appliance reliability, which [appliance] to buy, compare models, bird feeder camera, bird bath camera, smart garden devices.
| name | Tech Resume Optimizer |
| description | Optimize resumes for software engineering, PM, and technical roles |
Use this skill when the user:
What Tech Recruiters Look For:
1. Contact Information (including GitHub, Portfolio)
2. Professional Summary (optional but helpful)
3. Technical Skills (critical for ATS)
4. Work Experience (with technical achievements)
5. Projects (especially for early career)
6. Education
7. Certifications (if relevant)
John Developer
San Francisco, CA
john@email.com | (555) 123-4567
LinkedIn: linkedin.com/in/johndev
GitHub: github.com/johndev
Portfolio: johndev.io
Include:
Don't Include:
Option 1: By Category
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, Grafana
Option 2: By Proficiency (use carefully)
Expert: Python, React, PostgreSQL, AWS
Proficient: Go, TypeScript, MongoDB, Docker
Familiar: Rust, GraphQL, Kubernetes
Option 3: Flat List (ATS-friendly)
Skills: Python, JavaScript, TypeScript, React, Node.js, Django, PostgreSQL, MongoDB, AWS, Docker, Kubernetes, Git
Languages:
Frameworks/Libraries:
Databases:
Cloud/DevOps:
[Action Verb] + [Technical What] + [Scale/Impact] + [Technology Used]
Examples:
❌ Weak Technical Bullet:
- Worked on backend services
- Helped improve system performance
- Built features for the product
✅ Strong Technical Bullet:
- 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
Scale:
Performance:
Efficiency:
Business:
Software Engineer:
• 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%
Data Engineer:
• 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
DevOps/SRE:
• 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
Product Manager (Technical):
• 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 weekly
Critical for:
Project Name | Technologies | Link
• Description of what it does
• Technical highlights and challenges solved
• Scale or usage metrics if available
PROJECTS
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 pipeline
Do Include:
Don't Include:
B.S. Computer Science | Stanford University | 2020
GPA: 3.8/4.0 (include if above 3.5)
Relevant Coursework: Distributed Systems, Machine Learning, Database Systems
Software Engineering Certificate | App Academy | 2023
- 1000+ hour immersive program
- Full-stack JavaScript, React, Node.js, PostgreSQL
B.A. Economics | UCLA | 2020
Professional Certifications:
- AWS Solutions Architect Associate | 2023
- MongoDB Certified Developer | 2023
Relevant Education:
- MIT OpenCourseWare: Algorithms, Data Structures
- Coursera: Machine Learning Specialization (Stanford)
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:
# 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]
Remember: Your resume must pass ATS AND impress technical recruiters.
For ATS:
For Tech Recruiters: